16.3 A 28nm 384kb 6T-SRAM Computation-in-Memory Macro with 8b Precision for AI Edge Chips

Recent SRAM-based computation-in-memory (CIM) macros enable mid-to-high precision multiply-and-accumulate (MAC) operations with improved energy efficiency using ultra-small/small capacity (0.4-8KB) memory devices. However, advanced CIM-based edge-AI chips favor multiple mid/large capacity SRAM-CIM macros: with high input (IN) and weight (W) precision to reduce the frequency of data reloads from external DRAM, and to avoid the need for additional SRAM buffers or ultra-large on-chip weight buffers. However, enlarging memory capacity and throughput increases the delay parasitics on WLs and BLs, and the number of parallel computing elements; resulting in longer compute latency (tAC), lower energy-efficiency (EF), degraded signal margin, and larger fluctuations in power consumption across data-patterns (see Fig. 16.3.1). Recent SRAM-CIM macros tend to not use in-lab SRAM cells, with a logic-based layout, in favor of foundry provided compact-layout 8T [2], 3, [5] or 6T cells with local-computing cells (LCCs) [4], [6] to reduce the cell-array area and facilitate manufacturing. This paper presents a SRAM-CIM structure using (1) a segmented-BL charge-sharing (SBCS) scheme for MAC operations, with low energy consumption and a consistently high signal margin across MAC values (MACV); (2) An new LCC cell, called a source-injection local-multiplication cell (SILMC), to support the SBCS scheme with a consistent signal margin against transistor process variation; and (3) A prioritized-hybrid-ADC (Ph-ADC) to achieve a small area and power overhead for analog readout. A 28nm 384kb SRAM-CIM macro was fabricated using a foundry compact-6T cell with support for MAC operations with 16 accumulations of 8b-inputs and 8b-weights with near-full precision output (20b). This macro achieves a 7.2ns tAC and a 22.75TOPS/W EF for 8b-MAC operations with an FoM (IN-precision × W-precision × output-ratio × output-channel × EF/tAC) 6× higher than prior work. © 2021 IEEE.