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.
Authors
- Xin Si (ORCID: https://orcid.org/0000-0002-4993-0087)
- Jun Yang (ORCID: https://orcid.org/0000-0002-8379-0321)
- Yuyang Ye (ORCID: https://orcid.org/0000-0002-0726-0468)
- Tianzhu Xiong (ORCID: https://orcid.org/0009-0000-0618-374X)
Institutions
- Southeast University (CN)
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
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Fundamental Research Funds for the Central Universities