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07/27/2021
Griffin: Rethinking Sparse Optimization for Deep Learning Architectures
This paper examines the design space trade-offs of DNNs accelerators aim...
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06/16/2021
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator
Recent works demonstrated the promise of using resistive random access m...
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01/27/2021
Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators
In this paper, we propose a mixed-precision convolution unit architectur...
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01/31/2020
Near-Lossless Post-Training Quantization of Deep Neural Networks via a Piecewise Linear Approximation
Quantization plays an important role for energy-efficient deployment of ...
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03/10/2018