674339 event 1713894912 1713894972 Title:  Efficient and Robust Compute-in-Memory For Edge Intelligence
Committee:
Dr. Shimeng Yu, ECE, Chair, Advisor
Dr. Shaolan Li, ECE
Dr. Callie Hao, ECE
Dr. Muhannad Bakir, ECE
Dr. Celine Lin, CS
 

]]> The increasingly more complex artificial intelligence (AI) models have attracted interdisciplinary efforts to speed up AI workloads. On the hardware front, the disruptive paradigm of compute-in-memory (CIM) aims to process data directly inside memory arrays, which is a promising approach to overcome the memory wall problem that conventional platforms face. Complex arithmetic operations can be performed using compute-capable CIM arrays to achieve massive parallelism and high energy efficiency. This dissertation focuses on the circuit design and hardware architecture aspects of CIM for executing AI inference. First, I will present a mixed-signal CIM engine that performs parallelized matrix multiplications inside resistive random-access memory (RRAM) arrays. The RRAM-based CIM chip, fabricated in TSMC 40-nm node, demonstrates circuit techniques that ensure PVT-robust and secure computing. Next, I will discuss the unique opportunities to transform computing inside heterogeneous 3D stacks. An 3D-stacked CIM accelerator targeting vision transformer models is shown to gain form factor and efficiency benefits. Finally, I will present a low-power portable ultrasound system to showcase how CIM can be applied to embedded electronics. Through data volume reduction of the ultrasound frontend and the on-device intelligence inserted by CIM, significant power savings can be achieved for the portable imaging system.

]]> https://teams.microsoft.com/l/meetup-join/19%3ameeting_OGVlMzdkMTEtNjc5NS00YTYyLThhODQtYTliMjg1NTg1OTNh%40thread.v2/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%22bd6d4555-f571-4952-95e5-acd486eb4074%22%7d 434381 1788 100811 1808