학술논문

A 8-b-Precision 6T SRAM Computing-in-Memory Macro Using Segmented-Bitline Charge-Sharing Scheme for AI Edge Chips
Document Type
Periodical
Source
IEEE Journal of Solid-State Circuits IEEE J. Solid-State Circuits Solid-State Circuits, IEEE Journal of. 58(3):877-892 Mar, 2023
Subject
Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
Computing and Processing
Transistors
Common Information Model (computing)
SRAM cells
Logic gates
Common Information Model (electricity)
Artificial intelligence
Capacitance
Artificial intelligence (AI)
charge sharing
computing-in-memory (CIM)
inference
static random access memory (SRAM)
Language
ISSN
0018-9200
1558-173X
Abstract
Advances in static random access memory (SRAM)-CIM devices are meant to increase capacity while improving energy efficiency (EF) and reducing computing latency ( $T_{\mathrm {AC}}$ ). This work presents a novel SRAM-CIM structure using: 1) a segmented-bitline charge-sharing (SBCS) scheme for multiply-and-accumulate (MAC) operations with low energy consumption and a consistently high signal margin across MAC values; 2) a bitline-combining (BL-CMB) scheme to reduce the number of analog-to-digital converters (ADCs) and, thereby, provide options in determining a tradeoff between EF and inference accuracy; 3) a source-injection local-multiplication cell (SILMC) connected to two types of global-bitline-switch to support the SBCS and BL-CMB schemes with consistent signal margin against process variation in transistors; and 4) prioritized-hybrid ADC to suppress area and power overhead for analog readout operations. We fabricated a 28-nm 384-kb SRAM-CIM macro using foundry-provided compact-6T cells supporting MAC operations with 16 accumulations of 8-b input and 8-b weight with near-full precision output (20 b). This macro achieved $T_{\mathrm {AC}}$ of 7.2 ns and EF of 22.75 TOPS/W performing 8-b-MAC operations.