Task 001/002 - Neuro-inspired Algorithms and Theory

Abstract: In-Memory Computing (IMC) has become a promising paradigm for accelerating machine learning (ML) inference. While IMC architectures built on various memory technologies have demonstrated higher throughput and energy efficiency compared to conventional digital architectures, little research has been done from system-level perspective to provide comprehensive and fair comparisons of different memory technologies under the same hardware budget (area). Since large-scale analog IMC hardware relies on the costly analog-digital converters (ADCs) for robust digital communication, optimizing IMC architecture performance requires synergistic co-design of memory arrays and peripheral ADCs, wherein the trade-offs could depend on the underlying memory technologies. To that effect, we co-explore IMC macro design space and memory technology to identify the best design point for each memory type under iso-area budgets, aiming to make fair comparisons among different technologies, including SRAM, phase change memory, resistive RAM, ferroelectrics and spintronics. First, an extended simulation framework employing spatial architecture with off-chip DRAM is developed, capable of integrating both CMOS and nonvolatile memory technologies. Subsequently, we propose different modes of ADC operations with distinctive weight mapping schemes to cope with different on-chip area budgets. Our results show that under an iso-area budget, the various memory technologies being evaluated will need to adopt different IMC macro-level designs to deliver the optimal energy-delay-product (EDP) at system level. We demonstrate that under small area budgets, the choice of best memory technology is determined by its cell area and writing energy. While area budgets are larger, cell area becomes the dominant factor for technology selection.

Bio: Kang He is a third-year PhD student at the School of Electrical and Computer Engineering, Purdue University, under the supervision of Prof. Kaushik Roy. He is the recipient of Ross Fellowship at Purdue. He completed his M.S. degree at University of Pennsylvania in 2020, and was awarded the Master's Scholarship and Outstanding Research Award. He received his B.E. degree from Harbin Institute of Technology, China, in 2018. He is currently working on developing deep learning algorithms and efficient DNN accelerators.