Cited by 207Advanced Memory and Neural ComputingLow-power high-performance VLSI designParallel Computing and Optimization Techniques

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

Su, Jian-Wei, Chou, Yen-Chi, Liu, Ruhui, Liu, Ta-Wei, Lu, Pei-Jung, Wu, Ping-Chun, Chung, Yen-Lin, Hung, Li-Yang +13 more·2021·2021 IEEE International Solid-State Circuits Conference (ISSCC)

Abstract

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 tACand 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.

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Crossref · 10.1109/isscc42613.2021.9365984

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  • Most compute-in-memory or near-memory approaches based on SRAMs and digital compute do not address the energy and time costs of reloading weights, making direct side-by-side comparisons against NVM-based weight-stationary approaches difficult; several NVM compute-in-memory studies have focused on the macro-level without accounting for data transport, control or chip infrastructure costs, and are,

    “Although several compute-in-memory or near-memory approaches based on SRAMs and digital compute35–38 have been presented in the literature, most of these do not address the energy and time costs of reloading weights, thus making direct side-by-side comparisons against NVM-based weight-stationary approaches difficult.”

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Authors
Su, J.-W., Chou, Y.-C., Liu, R., Liu, T.-W., Lu, P.-J., Wu, P.-C., Chung, Y.-L., Hung, L.-Y., Ren, J.-S., Pan, T., Li, S.-H., Chang, S.-C., Sheu, S.-S., Lo, W.-C., Wu, C.-I., Si, X., Lo, C.-C., Liu, R.-S., Hsieh, C.-C., Tang, K.-T., & Chang, M.-F.
Affiliations
  • National Tsing Hua University (TW)
  • Industrial Technology Research Institute (TW)
Venue
2021 IEEE International Solid-State Circuits Conference (ISSCC)
Publisher
IEEE
Published
2021-02-13
Pages
250-252
ISSN
0193-6530 (ISSN-L)
OpenAlex
W3133754064
Cited by
207
References
0
Citations by year
2026: 21 · 2025: 35 · 2024: 34 · 2023: 54 · 2022: 52 · 2021: 11
FWCI
59.27
Type
conference paper
Language
en
Open access
closed
Locations
Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference (publishedVersion)
Funders
Nova
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
  • Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
Keywords
in-memory computing, 6T SRAM, multiply-accumulate operations, energy efficiency, process variation, analog readout

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