New Computing Breakthrough achieves 100 MILLION Times GPU Performance!

Summary

The video explores the shift from traditional digital computing to emerging probabilistic and thermodynamic computing. It highlights how these new paradigms, which embrace noise as a computational resource, promise up to 100 million times more energy efficiency than current technologies like NVIDIA GPUs. The video delves into how probabilistic computing leverages the randomness of the environment, inspired by principles like Boltzmann’s law, to solve complex AI and optimization tasks. Thermodynamic computing, using Josephson junctions and the laws of thermodynamics, is also discussed as a breakthrough in AI model training, with huge potential for faster and more efficient computations in areas like image generation and machine learning.

Takeaways

• 😀 Analog computers were once dominant but were complex, noisy, and inaccurate, leading to the shift towards digital computing in the 1960s.
• 😀 Digital computers, while precise and powerful, are approaching their physical limits, driving the need for new paradigms in computing.
• 😀 Probabilistic computing (or thermodynamic computing) is a new technology that embraces noise instead of fighting it, allowing for energy-efficient computation.
• 😀 This approach can reportedly provide up to 100 million times more energy efficiency than current NVIDIA GPUs for certain tasks.
• 😀 Traditional classical computers rely on binary bits (0 and 1), but the world is governed by probabilistic rules, which are more effective for tasks like optimization and prediction.
• 😀 Richard Feynman proposed that rather than forcing classical computers to simulate probabilistic systems, we should create a new type of computer that inherently works probabilistically.
• 😀 P-bits (probabilistic bits) are the key to probabilistic computing, fluctuating between 0 and 1 due to thermal energy, offering a balance between classical and quantum systems.
• 😀 P-bits operate similarly to coin flips, with the probability of outcomes varying and tunable for specific computational tasks, like optimization in AI and machine learning.
• 😀 Thermodynamic computing, a subset of probabilistic computing, uses the second law of thermodynamics and heat dissipation to perform computations based on entropy and energy minimization.
• 😀 Companies like Extropic and Normal Computing are pioneering the development of thermodynamic computers, which use Josephson junctions (JJ's) to create fast probabilistic bits.
• 😀 Extropic's thermodynamic computing approach can significantly speed up AI tasks like image generation and transformer models, offering massive energy efficiency improvements over classical systems.

Q & A

What is probabilistic computing, and how does it differ from classical computing?

Probabilistic computing is a new computing paradigm that embraces the natural noise in a system, using it as a resource to perform calculations. Unlike classical computing, which relies on deterministic bits (0s and 1s), probabilistic computing utilizes bits (p-bits) that naturally fluctuate between two states, allowing for more efficient problem-solving, especially for tasks involving uncertainty, like AI and simulations.

Why is probabilistic computing considered more energy-efficient than traditional computing?

Probabilistic computing is more energy-efficient because it leverages the noise in the system, reducing the need for large numbers of transistors and the energy required for simulating probabilistic behaviors on classical computers. This approach can reduce energy consumption by up to 100 million times compared to traditional GPUs.

What is the role of p-bits in probabilistic computing?

P-bits (probabilistic bits) are fundamental to probabilistic computing. Unlike classical bits that are either 0 or 1, p-bits fluctuate naturally between these two states due to their thermal energy. This randomness enables the system to explore multiple possible solutions, making it well-suited for tasks involving uncertainty, such as optimization and machine learning.

How does the Boltzmann law relate to probabilistic computing?

The Boltzmann law describes how particles distribute themselves in a system, favoring lower energy states. In probabilistic computing, this law is used to find the most probable state for a given system, with the system reaching equilibrium through fluctuations, similar to how molecules seek balance in a gas. This equilibrium is the solution to the computational problem.

What is the difference between p-bits and qubits?

P-bits and qubits are both types of bits used in non-classical computing systems. P-bits are probabilistic and fluctuate between two states due to thermal energy but operate in a classical manner, while qubits are quantum bits that can exist in a superposition of states (0 and 1 simultaneously), governed by quantum mechanics. P-bits are more energy-efficient and can operate at room temperature, unlike qubits, which often require low temperatures.

How do thermodynamic computers work, and why is thermodynamics relevant?

Thermodynamic computers harness the second law of thermodynamics, which states that the total entropy of an isolated system increases over time. These computers use noise as a computational resource to explore different states, allowing the system to reach an equilibrium that solves the problem. The natural dissipation of heat in these systems is a core aspect of their functionality.

What are Josephson junctions, and how are they used in thermodynamic computing?

Josephson junctions (JJs) are superconducting devices made of two superconductors separated by an insulating layer. They are used in thermodynamic computing to create probabilistic bits that fluctuate rapidly, generating noise and allowing the system to explore different possible solutions. These fluctuations are key to solving computational problems in thermodynamic systems.

Why do some thermodynamic computers require low temperatures, even though they rely on noise?

Although thermodynamic computers harness noise, they still rely on superconductivity, which occurs only at low temperatures. The low temperature is necessary for the Josephson junctions to function effectively and for the system to achieve the required stability and noise amplification for probabilistic computing.

What are the potential advantages of thermodynamic computers over traditional GPUs for AI tasks?

Thermodynamic computers are predicted to be up to 100 million times more energy-efficient than traditional GPUs for tasks like training neural networks and running diffusion-based models. This efficiency makes them highly attractive for AI and machine learning applications, where large computational resources are typically required.

Why won't probabilistic computers replace classical digital computers entirely?

Probabilistic computers are not suitable for all tasks. Applications requiring absolute precision, such as banking transactions or critical medical equipment, will still rely on classical digital computers. However, probabilistic computers excel in tasks with inherent uncertainty, like AI and Monte Carlo simulations, where they can outperform traditional systems.