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A 22nm 8Mb STT-MRAM Near-Memory-Computing Macro with 8b-Precision and 46.4-160.1TOPS/W for Edge-AI Devices
會議論文

A 22nm 8Mb STT-MRAM Near-Memory-Computing Macro with 8b-Precision and 46.4-160.1TOPS/W for Edge-AI Devices

Yen-Cheng Chiu, Win-San Khwa, Chung-Yuan Li, Fang-Ling Hsieh, Yu-An Chien, Guan-Yi Lin, Po-Jung Chen, Tsen-Hsiang Pan, De-Qi You, Fang-Yi Chen, …
Digest of technical papers - IEEE International Solid-State Circuits Conference, 卷.2023-, 頁碼.496-498
IEEE
2023 IEEE International Solid-State Circuits Conference (ISSCC) (San Francisco, CA, USA, 19/02/2023–23/02/2023)
19/02/2023

摘要

Artificial neural networks Bandwidth Hardware Memory management Energy Consumption Energy Efficiency Market Research
Nonvolatile-memory-based computing in memory (nvCIM) [1-6] is ideal for low-power edge-Al devices requiring neural network (NN) parameter storage in the power-off mode, a rapid response to device wake-up, and high energy efficiency for MAC operations (\text{EF}_{\text{MAC}}) . Current analog nvCIMs impose a tradeoff between the signal margin (SM) and the number of accumulations (\mathrm{N}_{\mathrm{A}\text{CU}}) per cycle versus \text{EF}_{\text{MAC}} and computing latency (\mathrm{T}_{\text{CD}-\text{MAC}}) . Near-memory computing (NMC), with high precision for inputs (IN), weights (W), and outputs (OUT), and a high \mathrm{N}_{\text{ACU}} is a trend to improve \text{EF}_{\text{MAC}}, \mathrm{T}_{\text{CD}-\text{MAC}} , and accuracy. A prior STT-MRAM NMC [1] uses vertical-weight mapping (VWM) to improve the \text{EF}_{\text{MAC}} ; however, further improvement is challenging: due to (1) the large energy consumption in reading repetitious weight data across multiple inputs for a single NN-layer; (2) a high bitstream toggling-rate (BTR) for digital MAC circuits (\text{DC}_{\text{MAC}}) reduces \text{EF}_{\text{MAC}} , and; (3) a limited SM and memory readout latency (\mathrm{T}_{\text{CD}-\mathrm{M}}) for memories with a small R-ratio (e.g. STT-MRAM, see Fig. 33.2.1). In developing an STT-MRAM nvCIM macro, this work moves beyond circuit-level novelty by using system-software-circuit co-design. This work achieves a high \text{EF}_{\text{MAC}} , a short \mathrm{T}_{\text{CD-M}} , a high read bandwidth (R-BW), a high IN-W-OUT precision, and a high \mathrm{N}_{\text{ACU}} by using the novel schemes: (1) a hardware based weight-feature aware read (WFAR) to reduce weight accesses and improve \text{EF}_{\text{MAC}} with a minimal area overhead; (2) toggling-aware weight-tuning (TAWT) to obtain fine-tuned weights (\mathrm{W}_{\text{FT}}) with a low BTR, which is based on VWM to enhance the \text{EF}_{\text{MAC}} of the \text{DC}_{\text{MAC}} ; (3) a differential charge-accumulating margin-enhanced voltage-sensing amplifier (DCME-VSA) to enhance the SM, while reducing the T CD - M . The proposed 22-nm S-Mb STT-MRAM NMC nvCIM macro achieves the highest R-BW (436\text{GB}/\mathrm{s}) and \text{EF}_{\text{MAC}}(46.4-160.1\text{TO}\text{PS}/\mathrm{W}) for \mathrm{N}_{\mathrm{A}\text{CU}}=576 for SblN - SbW - 26bOUT.

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