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20162021
most citedMixed Precision Training

996 citations · 1.1k across the 10 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

cs.CV2021★ 3 cited

Temporally Consistent Online Depth Estimation in Dynamic Scenes

Zhaoshuo Li, Wei Ye, Dilin Wang +4

Temporally consistent depth estimation is crucial for online applications such as augmented reality. While stereo depth estimation has received substantial attention as a promising…

cs.SD2021

Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet

Haichuan Yang, Yuan Shangguan, Dilin Wang +6

From wearables to powerful smart devices, modern automatic speech recognition (ASR) models run on a variety of edge devices with different computational budgets. To navigate the Pa…

cs.CL2021

Collaborative Training of Acoustic Encoders for Speech Recognition

Varun Nagaraja, Yangyang Shi, Ganesh Venkatesh +3

On-device speech recognition requires training models of different sizes for deploying on devices with various computational budgets. When building such different models, we can be…

cs.CL2021★ 1 cited

Noisy Training Improves E2E ASR for the Edge

Dilin Wang, Yuan Shangguan, Haichuan Yang +6

Automatic speech recognition (ASR) has become increasingly ubiquitous on modern edge devices. Past work developed streaming End-to-End (E2E) all-neural speech recognizers that can…

cs.LG2021★ 5 cited

Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization

David Eriksson, Pierce I-Jen Chuang, Samuel Daulton +7

When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device l…

cs.LG2021★ 14 cited

Accelerating Sparse Deep Neural Networks

Asit Mishra, Jorge Albericio Latorre, Jeff Pool +5

As neural network model sizes have dramatically increased, so has the interest in various techniques to reduce their parameter counts and accelerate their execution. An active area…

cs.SD2021

Memory-efficient Speech Recognition on Smart Devices

Ganesh Venkatesh, Alagappan Valliappan, Jay Mahadeokar +4

Recurrent transducer models have emerged as a promising solution for speech recognition on the current and next generation smart devices. The transducer models provide competitive…