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

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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.