Computation-In-Memory based Edge-AI for Healthcare: A Cross-Layer Approach

Speaker: Sumit Diware

Abstract: Recent advancements in artificial intelligence (AI) have driven the emergence of real-world cognitive products and services, which rely on neural networks to perform complex tasks. Edge computing for AI (edge-AI) combines data sources with local hardware that executes neural network computations, to improve the response latency, data privacy/security, and service reliability. Computation-in-memory (CIM) offers an energy-efficient and compact alternative to conventional neural network hardware for edge-AI, by enabling in-situ data processing with emerging memory technologies called memristors. Healthcare stands out as a key domain for CIM, due to its critical impact on society and the need for energy-efficient, compact hardware in healthcare edge applications. However, developing AI models for healthcare that are effective, accurate, and can fully reap CIM benefits remains a significant challenge. Moreover, memristors exhibit non-idealities that lead to errors during hardware execution. In this talk, I will describe our cross-layer research approach and contributions towards addressing these challenges. We first create effective, accurate, and CIM-oriented AI models for two healthcare applications: electrocardiogram (ECG) classification and diabetic retinopathy screening. We then devise mitigation strategies against memristor non-idealities and develop a system-on-chip tapeout as a holistic solution that covers the entire abstraction layer stack from application to fabrication.

Short Bio: Sumit Diware obtained Ph.D. from the Computer Engineering Group at Delft University of Technology (TU Delft), Netherlands, and M.Tech. in VLSI Design Tools and Technology (VDTT) from IIT Delhi. His research focuses on artificial intelligence (AI) processing architectures, with expertise in computation-in-memory, neuromorphic computing, emerging memory technologies, hardware-algorithm co-design, and system-on-chip (SoC) design/tapeout. He has authored/co-authored several publications in leading conferences such as DATE, DAC, and ICCAD, as well as IEEE journals including TBioCAS and TETCI. For his doctoral work, he recently received the European Design & Automation Association (EDAA) Outstanding Dissertation Award at DATE 2025 conference. Before his Ph.D., Sumit was a research assistant at the Karlsruhe Institute of Technology (KIT), Germany, where he worked on multicore SoC architectures. Prior to that, he worked at Qualcomm India as a part of IIT Delhi’s VDTT program, focusing on SoC power management architecture.