Collaborative Research: SHF: Medium: Memory-efficient Algorithm and Hardware Co-Design for Spike-based Edge Computing

在当今快速发展的人工智能（AI）世界中，能源效率已成为促进无处不在的智能系统发展的关键因素。人工智能的有效部署是克服功率受限设备所带来的限制的关键，并有助于可持续的技术进步。神经形态计算提供了一种由大脑启发的人工智能范式，称为脉冲神经网络（snn），它代表了人工智能可持续发展的有希望的一步。受大脑神经结构的启发，snn以稀疏、异步和事件驱动的模式处理信息，从而降低了功耗。该项目旨在将snn与现代集成电路集成在一起，提高各种人工智能领域的能效，如物体检测、自动驾驶和图像分类。该项目团队旨在通过原型芯片设计新颖的算法和硬件设计，以加速snn在低功耗和内存高效系统中的性能。这些脉冲神经芯片将使神经形态系统在无人机、自主机器人、便携式医疗设备和可穿戴智能助手等领域的实际和即时应用成为可能。此外，该项目采用了一种算法到系统的方法，为高中生、本科生和研究生提供了探索神经形态计算领域研究的机会。培养下一代科学家和工程师，促进人工智能和半导体领域的多样性，促进包容性也是该项目的核心。该项目解决了在具有严格内存和功率限制的边缘计算设备上实现深度学习和人工智能算法的关键任务。关键的创新在于利用大脑激发的峰值神经网络（SNN）方法进行边缘计算。该团队解决了尖峰神经元的内存开销问题，并采用了一种基本方法，优化了SNN在边缘设备上部署的算法和硬件设计。该项目提出了算法解决方案，包括具有共享计算和压缩策略的新架构，例如量化和早期退出。这些优化旨在提高snn在资源受限边缘设备上的效率。在硬件方面，该项目计划通过带有snn特定数据流、事件可寻址计算和对提议算法特性的可配置支持的原型芯片绦带来演示这些想法。目标是全面了解snn在边缘计算应用中的功率、性能和精度权衡，为可持续的人工智能铺平道路。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In today's rapidly advancing world of Artificial Intelligence (AI), energy efficiency has emerged as a crucial factor to facilitate the ubiquitous development of intelligent systems. The efficient deployment of AI holds the key to overcoming limitations posed by power-constrained devices and contributes to sustainable technological progress. Neuromorphic computing offers a brain-inspired paradigm of AI, called Spiking Neural Networks (SNNs), that represents a promising step forward in sustainable AI development. Inspired by the brain's neural architecture, SNNs process information in sparse, asynchronous, and event-driven patterns, resulting in reduced power consumption. This project aims to integrate SNNs with modern integrated circuits propelling energy efficiency across various AI domains, such as object detection, autonomous driving and image classification. The project team aims to devise novel algorithms and hardware design with prototype chips to accelerate the performance of SNNs in low-power and memory-efficient systems. These spiking neural chips will enable the practical and immediate application of neuromorphic systems in areas like drones, autonomous robots, portable medical devices, and wearable smart assistants. Furthermore, the project embraces an algorithm-to-system approach, providing opportunities for high school, undergraduate, and graduate students to explore research in the field of neuromorphic computing. An essential focus of this project also lies in training the next generation of scientists and engineers, fostering diversity, and promoting inclusivity within the AI and semiconductor fields. This project tackles the crucial task of enabling deep learning and AI algorithms on edge computing devices that have strict memory and power constraints. The key innovation lies in leveraging a brain-inspired spiking neural network (SNN) approach for edge computing. The team addresses the memory overhead issue of spiking neurons and takes a foundational approach, optimizing algorithms and hardware design for SNN deployment on edge devices. The project proposes algorithmic solutions, including novel architectures with shared computations and compression strategies, such as quantization and early exit. These optimizations aim to enhance the efficiency of SNNs on resource-constrained edge devices. On the hardware front, the project plans to demonstrate these ideas through prototype chip tapeouts with SNN-specific dataflow, event-addressable computations, and configurable support for proposed algorithm features. The goal is to develop a comprehensive understanding of the power, performance, and accuracy tradeoffs of SNNs for edge computing applications that will pave the way for sustainable AI.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.