HAINEM: Hardware Software Co-Design Solutions for Ultra-Efficient Artificial Intelligence and Neuromorphic Systems by Using Emerging Memory Technologies

传统的计算平台，特别是基于von-Neumann的计算体系结构，正受到处理单元和存储器之间有限的数据带宽的严重影响，这使得它们不能用于大数据处理和实现具有大量大参数集的最先进的机器学习(ML)算法。HAINEM计划将提出硬件和软件协同设计解决方案，以开发用于人工智能(AI)应用的超高效专业计算平台。在这个项目中，我们通过配置内存和处理来部署新兴的电阻开关(RS)内存技术设备的固有行为，以实现内存中计算(CIM)并解决称为内存墙的数据通信问题。我们将超越冯-诺伊曼应用程序处理单元(APU)，通过利用硬件和软件协同设计解决方案来加速ML算法并减少硬件非理想性。HAINEM将研究新的网络，并将现有的机器学习问题重新构建为适用于拟议的APU的更兼容的算法。HAINEM的第一个里程碑是开发EDGE ML硬件，通过在基于RS的交叉开关加速器中利用全并行模拟VMM的优势来提高计算效率(定义为TOPS/mm2)和能效(定义为TOPS/W)。它的目标是开发下一代ML硬件，这些硬件可以嵌入到靠近前端传感器网络的边缘，直接处理信号，而不是将它们发送到云中。该计划还将专注于神经形态计算概念，灵感来自最高效的计算系统人脑，以通过尖峰神经网络(SNN)结构进一步提高效率，朝着更并行、低功耗和健壮的计算平台迈进。在HAINEM中，我们展示了基于RS的系统如何模拟SNN中的功能原语，并可以在我们的大脑中复制突触和神经元模型，如学习行为作为生物突触，如长期抑制(LTD)和长期增强(LTP)。我们还将探索不同的新兴RS记忆设备作为突触连接，并将在这些突触设备上测试各种生物启发的学习机制，如基于对和基于三联体的尖峰时间依赖可塑性(STDP)。HAINEM还试图通过使用混合信号电路设计概念来开发更高效的基于RS的神经元电路。最后，我们的目标是在芯片上设计全功能的神经形态架构，概念验证将展示近传感器计算应用的实施，如EEG癫痫检测、基于EEG的大脑状态识别、视网膜数据处理。HAINEM将是一个重要的研究计划，帮助加拿大和多伦多成为人工智能和神经形态计算硬件实施的主要研发中心，与安大略省在基于软件的神经网络方面的世界级专业知识强有力的协同。

Conventional computing platforms, specially von-Neumann-based computing architectures, are seriously suffering from the limited data bandwidth between processing units and memory which makes them incompatible for big data processing and implementing state-of-the-art machine learning (ML) algorithms with extensive large sets of parameters. The HAINEM program will propose the hardware software co-design solutions to develop ultra-efficient specialized computing platforms for artificial intelligence (AI) applications. In this program, we deploy the intrinsic behaviour of emerging resistive switching (RS) memory technology devices by collocating memory and processing to enable compute-in-memory (CIM) and resolve the data communication problem known as memory wall. We will develop beyond von-Neumann application processing units (APUs) to accelerate ML algorithms and mitigating the hardware non-idealities by leveraging hardware software co-design solutions. HAINEM will investigate new networks and reframe existing machine learning problems into more compatible algorithms for the proposed APUs. HAINEM's very first milestone is to develop the edge ML hardware to improve computational efficiency (defined as TOPs/mm2) and energy efficiency (defined as TOPS/W) by getting the advantage from fully parallel analog VMM in RS-based crossbar accelerators. It aims to develop next generation ML hardware which could be embedded into the edge, near the front-end sensors networks, to process signals directly without sending them toward the cloud. This program will also focus on neuromorphic computing concepts inspired from the most efficient computing system, human brain, to further enhance the efficiency toward a more parallel, low-power, and robust computing platform through spiking neural network (SNN) structure. In HAINEM, we show how RS-based systems mimic functional primitives in SNN and can reproduce synaptic and neuronal models like learning behaviours as biological synapses in our brain such as long term depression (LTD) and long term potentiation (LTP). We will also explore different emerging RS memory devices as synaptic connections and will test various bio-inspired learning mechanisms over these synaptic devices like pair-based and triplet-based spike-time-dependant plasticity (STDP). HAINEM also try to develop more efficient RS based neuronal circuit by using mixed-signal circuit design concepts. Finally, our goal is to design fully functional neuromorphic architectures on chip and proof of concept will show the implementation of near-sensor computing applications such as EEG seizure detection, EEG-based brain state identification, retina data processing. HAINEM will be an important research program which helps Canada and Toronto to become a major research and development hub for hardware implementation for AI and neuromorphic computing in strong synergy with Ontario's world-class expertise in software-based neural networks.