A co-designed chip and training method, Topkima-Former, selects the top-k attention scores inside an in-memory converter to speed up softmax by up to 15x with a 0.4-1.2% accuracy drop.
DNN+ NeuroSim V2. 0: An end-to-end benchmarking framework for compute-in-memory accelerators for on-chip training,
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Topkima-Former: Low-energy, Low-Latency Inference for Transformers using top-k In-memory ADC
A co-designed chip and training method, Topkima-Former, selects the top-k attention scores inside an in-memory converter to speed up softmax by up to 15x with a 0.4-1.2% accuracy drop.