Energy Efficient AI Hardware: neuromorphic circuits and tools

Summary

In this presentation, Loretta Matia from the Fraunhofer Institute discusses the need for energy-efficient AI hardware for edge devices. She emphasizes the advantages of on-device AI, such as reduced latency and enhanced privacy, and the challenges of designing low-energy, low-latency ASICs for neural networks. Matia outlines the importance of neuromorphic computing and the six key areas of expertise required for its success, including system architecture, circuits, algorithms, and software tools. She also addresses the importance of accurate, energy-efficient computation at the circuit level and the need for use-case-based benchmarking.

Takeaways

• 🌐 Loretta is a department head at the Fraunhofer Institute for Integrated Circuits, focusing on energy-efficient AI hardware and neuromorphic circuits.
• 💡 The motivation for developing edge AI hardware is to reduce latency, increase energy efficiency, and enhance privacy by processing data locally rather than in the cloud.
• 🔋 Key advantages of edge AI include low latency due to local processing, higher energy efficiency by avoiding wireless data transmission, and improved privacy as data stays where it's generated.
• 📉 The push for edge AI is also driven by the end of Moore's Law, necessitating new computing paradigms like neuromorphic architectures to overcome limitations of traditional von Neumann architectures.
• 🛠️ Six critical areas of expertise for neuromorphic computing include system architecture, circuit design, algorithms, software tools, physical devices, and embedded non-volatile memories.
• 🔄 The presentation discusses an inference accelerator ASIC designed for neural networks with objectives like energy efficiency, speed, small area, and scalability to cover a broad range of use cases.
• 🔄 Analog memory computing is highlighted as a method to achieve high computation speed and energy efficiency by performing operations in-memory, leveraging the inherent parallelism of analog circuits.
• 📏 Hardware-aware training is crucial to deal with the non-idealities of analog computing, such as weight distribution mismatches, to ensure accurate and robust neural network models.
• 🔧 The importance of careful mapping of neural networks onto hardware is emphasized, as it significantly impacts performance metrics like latency and energy consumption.
• 📊 Benchmarking should be use-case based, focusing on energy per inference and inference latency rather than just top performance metrics, which may not reflect real-world application performance.

Q & A

What is the primary motivation for developing energy-efficient AI hardware?

The primary motivation is to bring AI to edge devices, IoT devices, and sensors where data is generated and collected. This allows for low latency, higher energy efficiency, and improved privacy since data does not need to be sent to the cloud.

What are the three main advantages of bringing AI to the edge?

The three main advantages are low latency, higher energy efficiency, and enhanced privacy. Low latency is achieved by processing data locally, energy efficiency is improved by not sending raw data wirelessly to the cloud, and privacy is enhanced because data remains where it's generated.

Why is it necessary to move away from conventional von Neumann architectures for AI hardware?

As Moore's Law is reaching its limits, conventional von Neumann architectures, which have a bottleneck between memory and computation, are no longer sufficient. Instead, architectures that compute close to or in the memory, like neuromorphic computing, are needed to achieve the required low latency and high energy efficiency.

What are the key objectives for an inference accelerator ASIC to be successful in the market?

The key objectives include being designed in established semiconductor processes, having ultra-low energy consumption per inference, being fast, having a smaller area for lower cost, and being configurable and scalable for a range of use cases.

Why is in-memory computing considered advantageous for AI hardware?

In-memory computing is advantageous because it allows for high parallelism and computation speed, and since there is no data movement as the computation happens on the memory, it significantly improves energy efficiency.

How does the presenter's team address the issue of inaccuracy in analog memory computing?

The team addresses inaccuracy through hardware-aware training, which involves quantizing the weights to reduce memory footprint and training the neural network model to be robust against hardware variances.

What is the importance of a mapping tool in the design of AI hardware?

A mapping tool is crucial as it determines how a neural network model is mapped onto the hardware. The strategy used for mapping can significantly impact performance indicators such as latency and energy consumption.

Why should benchmarking for AI hardware focus on use cases rather than just top performance metrics?

Benchmarking should focus on use cases because top performance metrics like TOPS per Watt can be misleading and do not reflect real-world performance. Use case-based benchmarking provides a more accurate comparison of how well the hardware performs for specific applications.

How does the presenter's team ensure their AI hardware is robust against environmental changes, especially temperature variations?

The team ensures robustness against environmental changes by performing corner simulations that cover a wide range of temperatures. They define temperature corners and simulate to ensure the hardware meets specifications across these extremes.

What is the significance of the tinyML community in the context of AI hardware benchmarking?

The tinyML community provides a framework for benchmarking AI hardware using neural network reference models. This allows for a standardized comparison of different hardware's performance on the same neural network models.