A Hybrid Domain and Pipelined Analog Computing Chain for MVM Computation

In this article, a stream-architecture and pipelined hybrid computing chain is presented to process matrix-vector multiplication (MVM). In each stage of the computing chain, a primary multiply-accumulate (MAC) stage consisting of charge, time, and digital domain processing units makes signed or unsigned <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times 1\times 8$ </tex-math></inline-formula> bit MAC operations and MSB quantization. Based on the stream architecture, the length of the computing chain can be configured to fit different MVM applications. In the charge-domain MAC unit, a double-plate sampling and weighted capacitor array with writing yield and efficiency enhanced 7T bitcell and three-step weighting scheme is implemented. To utilize the speed and resolution advantages of time-domain computing, a high linearity voltage-to-time converter (VTC) followed by a dynamic tristate delay chain is proposed to transfer and store MAC values from the charge domain in the time domain. To realize fast analog readout, a folding type and distributed time-to-digital converter (TDC) is proposed. To fully eliminate the offset and variation in the distributed TDC, a specific residue readout timing and back-end calibration scheme are applied. In the digital domain, a double-input and double-clock dynamic D flip-flop is built to realize partial sum transmission and accumulation in a single cycle with low energy and area consumption. Post-simulation results show that this computing chain can achieve 20.89–40.72-TOPS/W energy efficiency and 4.498-TOPS/mm2 throughput.

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Publication Details

Journal
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Published
2024-09-26
DOI
https://doi.org/10.1109/tvlsi.2024.3439355
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

A Hybrid Domain and Pipelined Analog Computing Chain for MVM Computation

Xin Si, Jun Yang, Yuyang Ye, Tianzhu Xiong
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Parallel Computing and Optimization Techniques
article

A Hybrid Domain and Pipelined Analog Computing Chain for MVM Computation

Xin Si, Jun Yang, Yuyang Ye, Tianzhu Xiong
article en

Abstract

In this article, a stream-architecture and pipelined hybrid computing chain is presented to process matrix-vector multiplication (MVM). In each stage of the computing chain, a primary multiply-accumulate (MAC) stage consisting of charge, time, and digital domain processing units makes signed or unsigned <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times 1\times 8$ </tex-math></inline-formula> bit MAC operations and MSB quantization. Based on the stream architecture, the length of the computing chain can be configured to fit different MVM applications. In the charge-domain MAC unit, a double-plate sampling and weighted capacitor array with writing yield and efficiency enhanced 7T bitcell and three-step weighting scheme is implemented. To utilize the speed and resolution advantages of time-domain computing, a high linearity voltage-to-time converter (VTC) followed by a dynamic tristate delay chain is proposed to transfer and store MAC values from the charge domain in the time domain. To realize fast analog readout, a folding type and distributed time-to-digital converter (TDC) is proposed. To fully eliminate the offset and variation in the distributed TDC, a specific residue readout timing and back-end calibration scheme are applied. In the digital domain, a double-input and double-clock dynamic D flip-flop is built to realize partial sum transmission and accumulation in a single cycle with low energy and area consumption. Post-simulation results show that this computing chain can achieve 20.89–40.72-TOPS/W energy efficiency and 4.498-TOPS/mm2 throughput.

IEEE Transactions on Very Large Scale Integration (VLSI) SystemsVol. 33(1)
Southeast University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 84%
Parallel Computing and Optimization Techniques
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