An SRAM-Based Hybrid Computation-in-Memory Macro Using Current-Reused Differential CCO

IEEE Journal on Emerging and Selected Topics in Circuits and Systems(2022)

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摘要
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 $96.05\times $ lower than state of the art.
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关键词
Convolutional neural network (CNN),SRAM,computation in memory (CIM),digital in-memory-array computing (DIMAC),phase-domain near-memory-array computing (PNMAC),differential current-controlled-oscillator (DCCO)
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