Abstract
This work presents a 4 kb 8T-SRAM computation-in-memory (CIM) macro based on hybrid computation using digital in-memory-array computing (DIMAC) and phase-domain near-memory-array computing (PNMAC). By employing multiple local dual-column arrays (LDCAs), bit-wise multiplications are computed digitally in memory with high energy efficiency and throughput. The PNMAC performs the summation and accumulation in parallel with a high dynamic range by using a proposed steering-DAC-based differential current-controlled-oscillator (DCCO). After the phase-domain accumulation is completed, only a one-time digital conversion needs to be performed using a phase quantizer with negligible phase-to-digital conversion overhead. Moreover, by effectively reusing the steered current to accumulate the multiplication results fed from the DIMAC, the power consumption of the PNMAC can be greatly reduced. The macro fabricated in a 65 nm process achieves 22.4TOPS/W peak energy efficiency and 19.03~mu text{W} power consumption with a 59.8% zero-skipping rate, which is 96.05times lower than state of the art.
| Original language | English |
|---|---|
| Pages (from-to) | 536-546 |
| Number of pages | 11 |
| Journal | IEEE Journal on Emerging and Selected Topics in Circuits and Systems |
| Volume | 12 |
| Issue number | 2 |
| DOIs | |
| State | Published - 1 Jun 2022 |
Bibliographical note
Publisher Copyright:© 2011 IEEE.
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This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Convolutional neural network (CNN)
- SRAM
- computation in memory (CIM)
- differential current-controlled-oscillator (DCCO)
- digital in-memory-array computing (DIMAC)
- phase-domain near-memory-array computing (PNMAC)
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