ASiM, a PyTorch-based simulator for SRAM analog compute-in-memory, reveals that ADC readout noise of about 1 LSB can severely degrade DNN inference accuracy, especially for Transformers and ImageNet, and that hybrid analog-digital execution or majority voting mitigates this.
A fully bit- flexible computation in memory macro using multi-functional computing bit cell and embedded input sparsity sensing,
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ASiM: Modeling and Analyzing Inference Accuracy of SRAM-Based Analog CiM Circuits
ASiM, a PyTorch-based simulator for SRAM analog compute-in-memory, reveals that ADC readout noise of about 1 LSB can severely degrade DNN inference accuracy, especially for Transformers and ImageNet, and that hybrid analog-digital execution or majority voting mitigates this.