Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware
@article{Blouw2020HardwareAT, title={Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware}, author={Peter Blouw and G. Malik and Benjamin Morcos and Aaron R. Voelker and C. Eliasmith}, journal={ArXiv}, year={2020}, volume={abs/2009.04465} }
Keyword spotting (KWS) provides a critical user interface for many mobile and edge applications, including phones, wearables, and cars. As KWS systems are typically 'always on', maximizing both accuracy and power efficiency are central to their utility. In this work we use hardware aware training (HAT) to build new KWS neural networks based on the Legendre Memory Unit (LMU) that achieve state-of-the-art (SotA) accuracy and low parameter counts. This allows the neural network to run efficiently… CONTINUE READING
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TinySpeech: Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices
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References
SHOWING 1-10 OF 19 REFERENCES
Laika: A 5uW Programmable LSTM Accelerator for Always-on Keyword Spotting in 65nm CMOS
- Computer Science
- ESSCIRC 2018 - IEEE 44th European Solid State Circuits Conference (ESSCIRC)
- 2018
- 22
14.1 A 510nW 0.41V Low-Memory Low-Computation Keyword-Spotting Chip Using Serial FFT-Based MFCC and Binarized Depthwise Separable Convolutional Neural Network in 28nm CMOS
- Computer Science
- 2020 IEEE International Solid- State Circuits Conference - (ISSCC)
- 2020
- 5
- Highly Influential
Vocell: A 65-nm Speech-Triggered Wake-Up SoC for 10- $\mu$ W Keyword Spotting and Speaker Verification
- Computer Science
- IEEE Journal of Solid-State Circuits
- 2020
- 3
- Highly Influential
Streaming keyword spotting on mobile devices
- Computer Science, Engineering
- INTERSPEECH
- 2020
- 5
- Highly Influential
- PDF
TinySpeech: Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices
- Computer Science, Engineering
- ArXiv
- 2020
- 4
- PDF
Always-On, Sub-300-nW, Event-Driven Spiking Neural Network based on Spike-Driven Clock-Generation and Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device
- Computer Science
- ArXiv
- 2020
- 1
- PDF
SRAM for Error-Tolerant Applications With Dynamic Energy-Quality Management in 28 nm CMOS
- Computer Science
- IEEE Journal of Solid-State Circuits
- 2015
- 44
- PDF
A 65 nm 1.0 V 1.84 ns Silicon-on-Thin-Box (SOTB) embedded SRAM with 13.72 nW/Mbit standby power for smart IoT
- Engineering, Computer Science
- 2017 Symposium on VLSI Technology
- 2017
- 3
How to Achieve World-Leading Energy Efficiency using 22FDX with Adaptive Body Biasing on an Arm Cortex-M4 IoT SoC
- Computer Science
- ESSDERC 2019 - 49th European Solid-State Device Research Conference (ESSDERC)
- 2019
- 4
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
- Computer Science
- NeurIPS
- 2019
- 20
- PDF