The Next Generation Of Brain Mimicking AI

Summary

This script delves into the energy-intensive nature of AI, highlighting the tech industry's growing concern over power consumption in AI models. It contrasts the inefficiency of current AI with the remarkable energy efficiency of the human brain, spurring the development of next-gen AI mimicking biological systems. The script explains artificial neural networks, their architectures, and training processes, before exploring spiking neural networks and neuromorphic computing as potential solutions for more energy-efficient AI. It concludes with an introduction to Brilliant, an interactive learning platform offering lessons in AI and other fields, promoting hands-on learning and critical thinking.

Takeaways

• 🔋 The tech industry's AI models are facing power consumption challenges, with a single GP4 textual request consuming as much energy as charging 60 iPhones.
• 🌡 A study predicts that by 2027, global AI processing could consume as much energy as Sweden, highlighting the need for more energy-efficient AI solutions.
• 🧠 The human brain is far more energy-efficient than current AI models, consuming only a fraction of the energy for intense mental activity compared to AI requests.
• 🏎️ There's a race to develop the next generation of AI that mimics human biology more closely, aiming to increase energy efficiency.
• 💡 Artificial neural networks, the basis for most AI systems, use complex statistical models and require significant computational power, leading to high energy consumption.
• 🔍 Different neural network architectures like convolutional and recurrent neural networks are used for different types of data processing tasks.
• 🔢 The training of AI models involves large amounts of mathematical computation, including matrix multiplication and calculus for optimizing parameters.
• 📈 The size and complexity of AI models, such as GPT-3, have grown exponentially, requiring immense computational power and energy for training and operation.
• 🆕 Third-generation AI research is exploring spiking neural networks that mimic biological systems more closely, offering potential energy efficiency benefits.
• 🕊️ Spiking neural networks are more energy-efficient due to their event-driven nature, generating activity only when necessary, unlike continuous computation in traditional networks.
• 🔬 Neuromorphic computing is an emerging field that aims to develop hardware architectures based on spiking neural networks, potentially revolutionizing AI with more efficient processing.

Q & A

What is the main concern regarding the tech industry's use of AI in terms of physical limitations?

The main concern is the high power consumption associated with both training and using AI models, which makes it one of the most energy-intensive computational processes.

How much energy does a single GP4 textual request consume compared to charging 60 iPhones?

A single GP4 textual request consumes around 30,000 watt-hours of energy, which is approximately the amount required to charge 60 iPhones.

What is the estimated energy consumption of global AI processing by 2027, according to a study at the Amsterdam School of Business and Economics?

By 2027, global AI processing is predicted to consume as much energy as Sweden, at around 131 GWatt hours per year.

How does the energy consumption of the human brain during intense mental activity compare to a basic GPT request?

During intense mental activity, the human brain consumes just one qu of a food calorie per minute, which is roughly equivalent to the energy consumption of a basic GPT request.

What does the stark contrast between the energy efficiency of biological neural systems and current AI models indicate?

The contrast indicates that the current approach of AI is unsustainable and grossly inefficient, sparking a race to develop a new generation of AI that more closely mimics our biology.

What are the two common architectures of artificial neural networks mentioned in the script?

The two common architectures mentioned are convolutional neural networks, designed for processing grid-like data such as images, and recurrent neural networks, structured for processing time-based sequential data.

How does the functionality of an artificial neural network arise?

The functionality of an artificial neural network arises from the interaction of information within the core of the network, known as the hidden layers, which are situated between the input and output layers.

What is the process used to adjust the weights and biases in an artificial neural network during training?

The process used to adjust the weights and biases is called gradient descent, an optimization algorithm that finds the values that minimize the cost function.

What is the significance of the number of floating point operations required for a single forward pass through the GPT-3 model?

The number of floating point operations required for a single forward pass through GPT-3 is in the order of trillions, indicating the massive computational power needed for such large neural networks.

What is the main advantage of spiking neural networks in terms of energy efficiency compared to traditional artificial neural networks?

Spiking neural networks are more energy efficient because they only generate spikes when necessary, leading to sparse activity and drastically reduced energy overhead compared to traditional networks.

What is neuromorphic computing, and how does it differ from traditional computing architecture?

Neuromorphic computing is a field of hardware computing architecture based on spiking neural networks, which physically recreates the properties of biological neurons. It differs from traditional computing by replacing synchronous data and instruction movement with an array of interconnected artificial neuron elements, each with localized memory and signal processing.

What are some of the key analog semiconductor technologies at the forefront of neuromorphic computing research?

Key technologies include memristors, phase change memory, ferroelectric field-effect transistors, and spintronic devices, which store and process information in ways that resemble biological synapses.

How does the TrueNorth neuromorphic chip differ from traditional microprocessors in terms of power consumption and architecture?

TrueNorth has a low power consumption of 70 mW and a power density 1,000 times lower than that of a conventional microprocessor. Its design allows for efficient memory computation and communication handling within each neurosynaptic core, bypassing traditional computing architecture bottlenecks.

What is the significance of the 'Halap' neuromorphic system introduced in 2024?

Halap is significant as it is the world's largest neuromorphic system, consisting of 1,152 Loihi processors, supporting up to 1.15 billion neurons and 128 billion synapses across 14,545 neuromorphic processing cores, while consuming just 2600 watts of power.

Outlines

00:00

🔋 AI's Energy Consumption Challenge

This paragraph discusses the growing concern over the energy consumption of AI models, highlighting that training and using AI is becoming one of the most energy-intensive processes. It provides a comparison, stating that a single GPT request consumes as much energy as charging 60 iPhones, which is 1,000 times more than a traditional Google search. The script also references a study predicting that by 2027, global AI processing could consume as much energy as Sweden. The human brain's efficiency during mental activity is contrasted with the inefficiency of current AI models, setting the stage for a new race to develop more energy-efficient AI systems that mimic biological systems.

05:00

🧠 The Basics of Artificial Neural Networks

This paragraph delves into the structure and function of artificial neural networks (ANNs), which form the basis of most AI systems. It explains the role of interconnected nodes or artificial neurons organized into layers, including input, hidden, and output layers. The paragraph details how data is processed through these layers using activation functions like sigmoid, tanh, and ReLU. The importance of weights and biases in storing information and the training process involving backpropagation and gradient descent are also covered. The computational intensity of ANNs, especially as network size increases, is emphasized, with examples of simple networks and large-scale models like GPT-3 to illustrate the point.

10:02

📉 The Evolution to Spiking Neural Networks

The script introduces the concept of spiking neural networks (SNNs) as a third-generation approach to AI that more closely mimics biological systems. It contrasts SNNs with traditional ANNs, explaining that SNNs communicate through discrete spikes or pulses, which is more energy-efficient. The advantages of SNNs include sparse activity and reduced energy overhead, as they only generate spikes when necessary. The paragraph also discusses the challenges of training SNNs due to their asynchronous nature and the difficulty in defining changes in spiking information propagation, making them unsuitable for traditional gradient descent-based training methods.

15:04

🌟 Neuromorphic Computing: The Future of AI Hardware

This paragraph explores neuromorphic computing, an emerging field focused on developing hardware architectures that physically recreate the properties of biological neurons. It discusses the limitations of traditional computing architectures for SNNs and the development of neuromorphic devices that feature localized memory and signal processing. The paragraph mentions key technologies like memristors, phase change memory, and spintronic devices that are at the forefront of neuromorphic research. It also highlights the introduction of neuromorphic chips like IBM's TrueNorth and Intel's Loihi, which offer significant energy efficiency and processing capabilities, paving the way for advances in fields like robotics and autonomous systems.

20:04

🚀 The Potential of Neuromorphic Systems

The script discusses the potential of neuromorphic systems, emphasizing their capacity for inference and optimization with significantly lower energy consumption and faster speeds than existing GPU-based architectures. It mentions the development of larger neuromorphic systems like Intel's Loihi HE2 chip and the Hailan Point system, which supports billions of neurons and synapses. The paragraph also touches on the ongoing research into analog-based AI chips and the optimistic outlook for a hybrid analog future, suggesting that neuromorphic systems will revolutionize AI by providing self-contained AI with advanced capabilities in various fields.

25:05

🎓 Learning About AI with Brilliant.org

The final paragraph shifts focus to the educational platform Brilliant.org, which offers interactive lessons in various fields, including AI. It highlights the platform's first principles approach to learning, which involves engaging with concepts through interactive problem-solving exercises. The script promotes a course on how large language models work, aiming to deepen understanding of these technologies. Brilliant.org is presented as a valuable resource for personal and professional development, encouraging continuous learning and the application of knowledge in real-world contexts.

Keywords

💡AI

AI, or Artificial Intelligence, refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the video, AI is the central theme, with a focus on its energy consumption and the development of more efficient models. The script discusses how the tech industry's reliance on AI is encountering physical limitations due to its high power consumption.

💡Energy Consumption

Energy Consumption is the amount of power used by a process or system over time. The script highlights that training and using AI models are extremely energy-intensive, with a single textual request consuming as much energy as charging 60 iPhones. This concept is crucial as it sets the stage for discussing the inefficiency of current AI models and the need for more sustainable approaches.

💡Artificial Neural Networks

Artificial Neural Networks (ANNs) are a set of algorithms designed to recognize patterns. They are inspired by the human brain and are the building blocks of most AI systems. The script explains that ANNs consist of interconnected nodes or artificial neurons structured into layers, which process information through a series of mathematical computations, contributing significantly to the high energy consumption of AI.

💡Spiking Neural Networks

Spiking Neural Networks (SNNs) are a type of neural network that more closely mimics the behavior of biological neurons. Unlike traditional ANNs, SNNs communicate through discrete spikes or pulses, with the timing of these spikes carrying information. The script discusses SNNs as a potential solution to the energy inefficiency of current AI models, offering a more sustainable approach to AI development.

💡Neuromorphic Computing

Neuromorphic Computing is an approach to building AI systems that are modeled after the neurobiological architecture of the human brain. The script introduces neuromorphic devices as a new field of hardware computing architecture that recreates the properties of biological neurons, offering a more energy-efficient alternative to traditional computing for AI systems.

💡Backpropagation

Backpropagation is a training algorithm used in artificial neural networks to calculate the gradient of the loss function with respect to each weight by error propagation. The script mentions backpropagation in the context of training neural networks, where it is used to adjust weights and biases to minimize the difference between predicted and actual outputs.

💡Gradient Descent

Gradient Descent is an optimization algorithm used to find the values of the parameters (weights and biases) that minimize the cost function in a neural network. The script describes gradient descent as the process by which weights and biases are adjusted during training to improve the accuracy of the network's predictions.

💡GPT (Generative Pre-trained Transformer)

GPT is a type of neural network architecture known as a Transformer, which is used for natural language processing tasks. The script uses GPT-3 as an example of a large language model to illustrate the immense computational power and energy consumption required for training and using such models.

💡Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are a class of deep neural networks widely used for analyzing visual imagery. The script mentions CNNs as an example of a neural architecture designed for processing grid-like data such as images, highlighting their role in AI's energy consumption.

💡Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are a class of neural networks that are designed to work with sequential data, such as time-series data or natural language. The script refers to RNNs as another common architecture for neural networks, which are structured to process time-based sequential data.

💡Brilliant.org

Brilliant.org is an online platform offering interactive lessons in various subjects, including math, data analysis, programming, and AI. The script mentions Brilliant.org as a resource for those interested in understanding the technology behind AI and improving their problem-solving skills through a first principles approach.

Highlights

Tech industry's obsession with AI is facing physical limitation in power consumption.

AI models are among the most energy-intensive computational processes.

A single GP4 textual request consumes as much energy as charging 60 iPhones.

By 2027, Global AI processing could consume as much energy as Sweden.

The human brain is far more energy-efficient than current AI models.

AI's intense power consumption is due to underlying artificial neural networks.

Artificial neural networks are composed of interconnected nodes or artificial neurons.

Convolutional and recurrent neural networks are common architectures for specific data types.

Activation functions like sigmoid, tanh, and ReLU are used in neural networks.

Weights and biases in neural networks store information and create functionality.

Backpropagation and gradient descent are used to train neural networks.

GPT3, a large language model, has 175 billion parameters and requires significant computational power.

Spiking neural networks aim to mimic biological systems more closely for energy efficiency.

Spiking neural networks communicate through discrete spikes or pulses.

Neuromorphic computing is a new field of hardware computing architecture based on spiking neural networks.

Neuromorphic devices like TrueNorth and Loihi offer energy efficiency and real-world problem-solving capabilities.

Halap Point, the world's largest neuromorphic system, supports up to 1.15 billion neurons and 128 billion synapses.

Brilliant.org offers interactive lessons in math, data analysis, programming, and AI to develop problem-solving skills.

Transcripts

this episode is brought to you by

brilliant the tech industry's obsession

with AI is beginning to hit its first

physical limitation power consumption

both training and using AI models is

proving to be one of the most energy

intensive collection of computational

processes that is consumed by the public

at large in fact it's estimated that a

single gp4 textual request consumes

around 30 00 wat hours of energy or

about the amount required to charge 60

iPhones this is roughly 1,000 times more

energy than what is needed to perform a

traditional non- AI based request by

Google search one study at the Amsterdam

School of Business and economics

predicts that by 2027 at its current

trajectory Global AI processing will

consume as much energy as Sweden at

around 131 gwatt hours per year when the

energy consumption of the human brain is

examined it becomes clear that the

current approach of AI is unsustainable

and grossly inefficient during intense

mental activity a brain consumes just

one qu of a Food calorie per minute this

equates to about 17 hours of intense

thought for about the same energy

consumption of a basic GPT for request

this stark contrast between the Energy

Efficiency of biological neuros systems

and Kent AI models has created a new

race to develop the next generation of

AI one that more closely mimics our

biology the intense power consumption of

AI is a direct result of the underlying

models of artificial neural networks

that the vast majority of AI systems are

composed of artificial neural networks

Loosely emulate biological systems in

their approach to problem solving using

a complex statistical model to best

appro imate a solution the basic

structure of an artificial neural

network consists of a network of

interconnected nodes or artificial

neurons structured into organized layers

data is fed into the network through an

input layer each neuron in the input

layer represents an input feature or

variable the complexity of this input

layer depends on the dimensionality of

the input data and its complexity and

Fidelity the functionality of an

artificial neural network comes from the

inter interaction of information within

the core of the network known as the

hidden layers hidden layers are situated

between the input and output layers and

can be structured into a wide variety of

configurations depending on the

complexity of the task the network is

designed for two common architectures

for example are convolutional neural

networks which are designed for

processing grid-like data such as images

and recurrent neural networks which are

structured for processing time-based

sequential data

the choice of neural architecture and

hyperparameters such as the number of

layers number of neurons per layer and

activation functions depend on the

complexity of the task the available

training data and the desired

performance within each hidden layer are

multiple neurons that process and

transform the data received from the

previous layer this occurs through the

application of an activation function to

the weighted sum of the inputs while

many types of activation functions exist

the three most commonly used are sigmoid

tan and the highly favored rectified

linear unit function the hidden layers

interfac to an output layer which

produces the final predictions or

outcomes of the network the number of

neurons in the output layer depends on

the task the network is designed for and

the desired output format neurons and

adjacent layers of the network are

connected with an Associated weight that

determines the strength and importance

of the signal passing through it during

training the weights are adjusted to

minimize the difference between the

predicted outputs and the actual targets

additionally each neuron in the hidden

and output layers also has a bias term

associated with it the bias term acts as

an additional input to the neuron and

helps to shift the activation function

providing flexibility in the Network's

learning process in effect weights and

biases store information within the

network and create its functionality the

flow of information in an artificial

neural network typically flows forward

from the input layer through the hidden

layers to the output layer in order for

the weights and biases to take on values

that perform the intended task a neural

network must be trained for the majority

of artificial neural network

applications a labeled data set is used

to train a network in this process a

known piece of training data is fed into

the network and its output compared to

the training data a cost function is

used to measure any difference

indicating how accurately the network

model performs from this cost function a

technique known as back propagation is

used to propagate this error backwards

from the output to the input layer this

error is used to calculate the gradient

of the cost function with respect to

each weight and bias it helps determine

how much they need to be changed to

produce a more accurate output the

weights and bies are then adjusted using

a process called gradient descent

gradient descent is an optimized ization

algorithm that is used to find the

weights and biases that minimize the

cost function pulling the network closer

to more accurate outputs based on the

training data at its core an artificial

neural network is a complex mathematical

function that Maps input features to

Output predictions the weights and

biases of the network represent the

parameters of this function and the goal

of training is to find the optimal

values of these parameters that minimize

the difference between between the

predicted output and the actual

targets the training and use of

artificial neural networks involve a

large amount of mathematical computation

primarily in the form of matrix

multiplication for propagating parameter

effect within the network and calculus

for back propagation passes where

updates to weights and biases are made

through gradient descent the number of

Matrix multiplications scales with the

number of layers and neurons in the

network while the number of gradient

computations scales with the number of

weights and biases let's look at a tiny

simple feedforward neural network with

an input layer of 1,000 neurons one

hidden layer of 500 neurons and an

output layer of just 10 neurons during a

Ford pass this network would involve

500,000 multiplications and additions

for the first layer and 5,000

multiplications and additions for the

second layer as the network gets larger

and utilizes more layers the computing

power required Skyrock ETS the V16

architecture for example a convolutional

neural network renowned for its

Simplicity and Effectiveness in image

classification and object detection has

just 16 layers yet involves over 138

million parameters the number of

floating Point operations required for a

single Ford pass through the network is

in the order of billions as artificial

neural networks grow to the levels

required for industry changing large

language model AIS the computing power

required becomes staggering to

illustrate the magnitude of computation

involved in large neural networks let's

consider the already outdated gpt3 model

developed by open AI in 2020 gpt3

launched as one of the largest language

models at the time bringing public

awareness to the incredible power of

artificial neural networks at these

scales gpt3 at its core is based on a

type of neural network architecture

known as a Transformer that consists of

96 layers each with

12,288 neurons Transformer networks use

self attention mechanisms to capture

dependencies between words in a sequence

weighing the importance of different

input parts for predictions when all the

supporting mechanisms are considered

gpt3 has a staggering 175 billion

parameters which include the weights and

biases of the network to put this into

perspective if each parameter was stored

as a 32-bit floating Point number the

model would require approximately 700 GB

of memory training gpt3 required an

immense amount of computational power it

was trained on a cluster of 1224 Nvidia

a100 gpus collectively creating up to

320 POF flops of mixed Precision

performance the training process

involved feeding the model with a vast

Corpus of text Data comprising

approximately 45 tabt of compressed

plain text during training the model

processed hundreds of billions of

individual words or subwords know as

tokens the training process took several

weeks to complete consuming a

significant amount of energy and

computational resources some estimates

placed the energy consumption of

training at around 220 megawatt hours or

enough to power about 20 average US

homes for a year even after training a

single Ford pass through the gpt3 model

involves a massive number of Matrix

operations with 175 billion parameters

and 96 layers the number of floating

Point operations required for a single

Ford pass through gpt3 is estimated to

be in the order of trillions for example

to process a sequence of 1,000 tokens

gpt3 would require approximately 400

teraflops in 2024 it's estimated that

all leading models operate on well past

a parameters and this is expected to

continue growing until a point of

diminished returns is reached with

energy consumption being a primary

element in this limit currently most AI

is derived from the second generation of

artificial neural network development

characterized by its primary focus on

deep learning but to push past the

energy requirements associated with it

current research on third generation

networks is focused on a concept that

mimics biological systems much closer

with spiking neural networks while

inspired by biological neurons current

artificial neural networks are

simplified models that do not fully

capture the complexity of biological

systems spiking neural networks aim to

bridge this Gap by communicating through

discrete spikes or pulses with timing of

these spikes carrying information when

compared to the continuous use of

activation functions to compute an

output based on the weighted sum of

inputs

the spiking method of information

transmission more closely resembles

biological neurons operating on discrete

events that occur at a certain point of

time spiking neural networks receive a

series of spikes or a spike train as

input and produce a spike train as the

output one of the largest advantages to

this approach is in Energy Efficiency

current artificial neural networks

require a constant recalculation of the

entire network whenever changes occur

making its reaction to new information

incredibly energy intensive in contrast

spiking neural networks much like

biological systems only generate spikes

when necessary leading to sparse

activity and drastically reduced Energy

overhead spiking neural networks behave

drastically different from traditional

activation function-based models in that

their memory and functionality operates

analogous to the membrane potential

mechanism of biological neurons while

several neuron models for spiking neural

networks exist for determining the

relationship between neural membrane

potential at the input stage and

membrane potential at the output stage

the most commonly used model is the

Leaky integrate and fire threshold model

in this model the membrane potential

equivalent in a spiking neural network

can be increased by excitatory spikes

and decreased by inhibitory spikes it

also exhibits Decay over time simulating

the leakage of electrical charge in

biological neurons if a neuron's

membrane potential exceeds a threshold

the neuron will send a single impulse to

each connected Downstream neuron after

generating a spike the neuron's membrane

potential is reset to a resting value

after firing a spike the neuron enters a

refractory period during which it cannot

generate another Spike the refractory

period simulates the biological

constraints of neurons requiring time to

recover before firing again because of

of their event-driven nature spiking

neural networks produce a continuous

asynchronously driven output that reacts

dynamically with inputs and the

Network's internal structure this is

dramatically different from the large

parameter function model of traditional

artificial neural networks that produce

a real number output instead of a

calculated gradient descent into a

solution spiking neural networks

approach a goal by dynamically reaching

an equilibrium over time within its

Network spiking neural network networks

communicate with far more information

than traditional artificial neural

networks due to the timing element of

the spiking process known as temporal

coding these Spike trains can represent

information in a broad range of

encodings from simple pulse rates to

elaborate timing patterns and even

multi-layered coordinated patterns with

other neuron groups it's theorized that

these temporal interactions among groups

of neurons create emergent signal

processing patterns that can potentially

replace place the equivalent

functionality of hundreds of neurons in

traditional artificial neural networks

because time is an encoded property of

spiking neural networks information flow

it's well suited for processing

continual real world sensory information

such as spatial temporal data and motion

control it can also accomplish this with

less Network complexity and Incredibly

low processing latencies all while

eliminating the need for the recurrent

structures that introduce temporal

awareness intr traditional artificial

neural networks while incredibly

powerful and versatile spiking neural

networks exhibit an inherent

incompatibility with current artificial

neural network technology reliably

encoding and decoding traditional data

through a spiking neural network in

pulse trains is proving to be difficult

though various experimental methods

exist for coding real numbers as Spike

trains such as rate codes or frequency

of spikes time to First Spike and the

interval between spikes even within the

realm of neurobiology research is still

ongoing as to how exactly sensory

information is encoded processed and

reacted to all within 10 milliseconds a

response time that supersedes what's

possible with basic coding methods

spiking neural networks also suffer from

a fundamental incompatibility with

current artificial neural network

training techniques because of the

asynchronous nature of spiking neural

networks and the difficulty in

mathematically defining change in

spiking information propagation with

within the network spiking neural

networks are unsuitable for traditional

artificial neural network gradient

descent-based training methods that

perform error back propagation when

combined with the challenges of

information coding spiking neural

networks are proving to be challenging

to train in a supervised manner where

labeled data is used to provide a

specific functionality from the network

in fact to date there is no effective

supervised Training Method that is

suitable for spiking neural networks

that has Prov provided better

performance than second generation

networks however they have been

demonstrated to be a viable option for

unsupervised biologically inspired

training methods that work best with

generalized prediction clustering and

Association of

information because traditional

artificial neural networks are

effectively massive math problems they

work well with classic Computing

architecture in which a system

encompasses a clocked interconnection of

CPU memory storage and IO that exchanges

data and instructions back and forth in

a sequential manner to perform

computation this heavy Reliance on

matrices allows artificial neural

networks to scale well with parallel

Computing for large scale artificial

neural networks this is accomplished

using thousands to millions of processor

cores using gpus or dedicated GPU like

parallel Computing processors spiking

neural networks however do not perform

as well on traditional Computing

architecture and cannot scale easily on

them they are asynchronous behavior and

Reliance on localized timing

Independence makes emulating their

behavior in software computationally

expensive due to the overhead created by

the fundamental incompatibility and how

information flows through them when

compared to traditional Computing

architecture while currently this

hinders their use at larger scales that

can rival current artificial neural

network capabilities it has led to the

research and development into an

entirely New Field of Hardware Computing

architecture based on biking neural

networks known as neuromorphic Computing

neuromorphic devices are based around a

processing architecture that physically

recreates the properties of a biological

neuron this paradigm shift in Computing

replaces the synchronous monolithic

movement of data and instructions

between separate processors and memory

with a large array of interconnected

artificial neuron elements each with

their own localized memory and Signal

processing neuromorphic devices can be

based on a broad range of mediums such

as chemical and fluid systems but

semiconductor-based mixed mode analog

digital ic's are the focus of current

research while traditional full digital

Computing can be applied to the concept

researchers are looking towards analog

Computing based on historis or the

dependence of the state of a system on

its history to create neuronlike

functionality within these devices

analog processing eliminates the

complexity and latency of digital

architecture by deriving AR icial neuron

functionality from the physical

properties of a semiconductor component

directly this creates an extremely fast

reacting Computing element with orders

of magnitude less power

consumption while analog Computing has

always been too noisy and inconsistent

for traditional Computing much like in

biological systems timecode spiking

signals are far more resilient in a

noisy and irregular signal environment

currently a few key analog semiconductor

technologies that store and prod process

information in a way that resembles the

behavior of biological synapses are at

the Forefront of semiconductor-based

artificial neuron research memers are

two terminal devices that change their

resistance based on the amount of

current that has flown through them

phase change memory consists of calogen

material sandwiched between two

electrodes when a voltage is applied the

material heats up and changes its phase

from Amorphis to crystalline changing

its electrical

resistance ferroelectric Field Effect

transistors are three terminal devices

that use a ferroelectric material as the

gate dialectric when a voltages appli to

the gate the ferroelectric material

polarizes changing the conductivity of

the channel between the source and drain

electrodes spintronic devices use the

spin of electrons to store and process

information they typically consist of a

magnetic material sandwiched between two

non-magnetic electrodes when a current

is passed through the device the spin of

the electrons aligned with the magnetic

field changing the devices

resistance by 2014 the first

neuromorphic chip would be introduced

called True North True North consists of

4,096 cores each containing 256

programmable simulated neurons totaling

just over a million neurons each neuron

has 256 programmable synapses that

transmit signals between them resulting

in over 268 million programmable synap

true North's design allows for efficient

memory computation and communication

handling within each neurosynaptic core

bypassing traditional Computing

architecture bottlenecks this results in

a low 70 M power consumption and a power

density 1,000th that of a conventional

microprocessor in 2017 Intel would

introduce loow e a neuromorphic chip

fabricated using Intel's 14 nanometer

process that features 128 clusters of

1,2 4 artificial neurons each totaling

131,072 simulated neurons and around 130

million synapses although less powerful

than IBM's True North it offered far

more flexibility becoming a powerful

tool for energy efficient real world

spiking neural network-based problem

solving research by September 2021 lye 2

would be released featuring over a

million simulated neurons with faster

speeds higher bandwidth intership

Communications increas capacity a more

compact size and improved

programmability compared to its

predecessor the lowy HE2 chip would

become the basis for the h point in 2024

becoming the world's largest neomorphic

system consisting of

1,152 lowy HE2 processors the system

supports up to 1.15 billion neurons and

128 billion synapses across

14,545 neuromorphic processing course

while consuming just 2600 watts of power

it also includes over 2,300 embedded x86

processors for ancillary computations

it's estimated that halap point has the

neuron capacity of roughly equivalent to

that of an alow brain or the cortex of a

capucin monkey L he 2 based systems have

demonstrated the ability to perform

inference and optimization using 100

times less energy at speeds up to 50

times faster than existing GPU based

architecture as of 2024 despite ongoing

research there are no commercially

available analog-based AI chips though

some research-based and smallscale ic's

have been developed while neuromorphic

development is pushing forward on mature

digital architecture the industry is

optimistic about a breakthrough into a

hybrid analog future as research

progresses we can expect neuromorphic

systems with greater neuron capacities

faster processing speeds and improved

Energy Efficiency to revolutionize the

AI field with self-contained AI

drastically advancing in fields such as

Robotics and autonomous systems within

the coming

years as deeper models and more

sophisticated Hardware evolve in the

pursuit of a third generation of neural

networks the missing link between

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