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12/06/2022
Enabling and Accelerating Dynamic Vision Transformer Inference for Real-Time Applications
Many state-of-the-art deep learning models for computer vision tasks are...
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06/13/2022
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
Data clipping is crucial in reducing noise in quantization operations an...
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06/26/2021
Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update
Representing deep neural networks (DNNs) in low-precision is a promising...
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03/16/2021
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers
Transformers have transformed the field of natural language processing. ...
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02/12/2021
Verifying High-Level Latency-Insensitive Designs with Formal Model Checking
Latency-insensitive design mitigates increasing interconnect delay and e...
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02/08/2021
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference
Quantization enables efficient acceleration of deep neural networks by r...
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05/23/2017
