192 citations · 731 across the 30 of their papers we have counts for
9 papers · 1 filter
Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration
Zhi-Gang Liu, Paul N. Whatmough, Matthew Mattina
Convolutional neural network (CNN) inference on mobile devices demands efficient hardware acceleration of low-precision (INT8) general matrix multiplication (GEMM). Exploiting data…
Rank and run-time aware compression of NLP Applications
Urmish Thakker, Jesse Beu, Dibakar Gope +2
Sequence model based NLP applications can be large. Yet, many applications that benefit from them run on small devices with very limited compute and storage capabilities, while sti…
High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands
Dibakar Gope, Jesse Beu, Matthew Mattina
Matrix multiplications between asymmetric bit-width operands, especially between 8- and 4-bit operands are likely to become a fundamental kernel of many important workloads includi…
Efficient Residue Number System Based Winograd Convolution
Zhi-Gang Liu, Matthew Mattina
Prior research has shown that Winograd algorithm can reduce the computational complexity of convolutional neural networks (CNN) with weights and activations represented in floating…
TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids
Igor Fedorov, Marko Stamenovic, Carl Jensen +5
Modern speech enhancement algorithms achieve remarkable noise suppression by means of large recurrent neural networks (RNNs). However, large RNNs limit practical deployment in hear…
Systolic Tensor Array: An Efficient Structured-Sparse GEMM Accelerator for Mobile CNN Inference
Zhi-Gang Liu, Paul N. Whatmough, Matthew Mattina
Convolutional neural network (CNN) inference on mobile devices demands efficient hardware acceleration of low-precision (INT8) general matrix multiplication (GEMM). The systolic ar…
Searching for Winograd-aware Quantized Networks
Javier Fernandez-Marques, Paul N. Whatmough, Andrew Mundy +1
Lightweight architectural designs of Convolutional Neural Networks (CNNs) together with quantization have paved the way for the deployment of demanding computer vision applications…
Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation
Chuteng Zhou, Prad Kadambi, Matthew Mattina +1
The success of deep learning has brought forth a wave of interest in computer hardware design to better meet the high demands of neural network inference. In particular, analog com…
Compressing Language Models using Doped Kronecker Products
Urmish Thakker, Paul N. Whatmough, Zhi-Gang Liu +2
Kronecker Products (KP) have been used to compress IoT RNN Applications by 15-38x compression factors, achieving better results than traditional compression methods. However when K…