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RAMAN: A Re-configurable and Sparse tinyML Accelerator for Inference on Edge
In this paper, we present RAMAN, a Re-configurable and spArse tinyML Accelerator for infereNce on edge, architected to exploit the sparsity to reduce area (storage), power as well as latency.
DBQ: A Differentiable Branch Quantizer for Lightweight Deep Neural Networks
While various complexity reduction techniques, such as lightweight network architecture design and parameter quantization, have been successful in reducing the cost of implementing these networks, these methods have often been considered orthogonal.
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