PICO-RAM: A PVT-Insensitive Analog Compute-In-Memory SRAM Macro With In Situ Multi-Bit Charge Computing and 6T Thin-Cell-Compatible Layout
Abstract
Analog compute-in-memory (CIM) in static random access memory (SRAM) is promising for accelerating deep learning inference by circumventing the memory wall and exploiting ultra-efficient analog low-precision arithmetic. Latest analog CIM designs attempt bit-parallel (BP) schemes for multi-bit analog matrix-vector multiplication (MVM), aiming at higher energy efficiency, throughput, and training simplicity and robustness over conventional bit-serial (BS) methods that digitally shift-and-add multiple partial analog computing results. However, BP operations require more complex analog computations and become more sensitive to well-known analog CIM challenges, including large cell areas, inefficient and inaccurate multi-bit analog operations, and vulnerability to PVT variations. This article presents PICO-RAM, a PVT-insensitive and compact CIM SRAM macro with charge-domain BP computation. It adopts a multi-bit thin-cell …
Zhiyu Chen
PhD 2023, now at Apple
Ziyuan Wen
Ph.D. Student (started in 2022)
Akhil Pakala
MECE 2024, now at Apple
Yiwei Zou
Ph.D. Student (started in 2022)
