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02/14/2021
Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices
Structured matrices, such as those derived from Kronecker products (KP),...
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10/06/2020
Rank and run-time aware compression of NLP Applications
Sequence model based NLP applications can be large. Yet, many applicatio...
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08/03/2020
High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands
Matrix multiplications between asymmetric bit-width operands, especially...
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01/24/2020
Compressing Language Models using Doped Kronecker Products
Kronecker Products (KP) have been used to compress IoT RNN Applications ...
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11/04/2019
Ternary MobileNets via Per-Layer Hybrid Filter Banks
MobileNets family of computer vision neural networks have fueled tremend...
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10/04/2019
Pushing the limits of RNN Compression
Recurrent Neural Networks (RNN) can be difficult to deploy on resource c...
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06/12/2019
Run-Time Efficient RNN Compression for Inference on Edge Devices
Recurrent neural networks can be large and compute-intensive, yet many a...
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06/07/2019
Compressing RNNs for IoT devices by 15-38x using Kronecker Products
Recurrent Neural Networks (RNN) can be large and compute-intensive, maki...
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04/05/2019
Measuring scheduling efficiency of RNNs for NLP applications
Recurrent neural networks (RNNs) have shown state of the art results for...
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03/04/2019
