192 citations · 730 across the 29 of their papers we have counts for
8 papers · 1 filter
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference
Thierry Tambe, Coleman Hooper, Lillian Pentecost +8
Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks. However, their hefty computation…
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…
Mesorasi: Architecture Support for Point Cloud Analytics via Delayed-Aggregation
Yu Feng, Boyuan Tian, Tiancheng Xu +2
Point cloud analytics is poised to become a key workload on battery-powered embedded and mobile platforms in a wide range of emerging application domains, such as autonomous drivin…
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…
CHIPKIT: An agile, reusable open-source framework for rapid test chip development
Paul Whatmough, Marco Donato, Glenn Ko +3
The current trend for domain-specific architectures (DSAs) has led to renewed interest in research test chips to demonstrate new specialized hardware. Tape-outs also offer huge ped…