Always-On Speech Recognition Using TrueNorth, a Reconfigurable, Neurosynaptic Processor

Tsai, Wen-Fa; Davis, Richard; Cassidy, Andrew; DeBole, Michael; Andreopoulos, Alexander; Jackson, Bryan L.; Flickner, Myron; Arthur, John V.; Modha, Dharmendra S.; Sampson, John J.; Narayanan, Vijaykrishnan

doi:10.1109/tc.2016.2630683

Cited by 28 publications

(17 citation statements)

References 33 publications

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“…With regards to the latter we perceive two main trends. First, adapt deep learning methods [2] to spiking neural networks (for instance [3]). Second, transfer computational mechanisms known from biological brains to neuromorphic hardware [4].…”

Section: Introductionmentioning

confidence: 99%

Reservoir Transfer on Analog Neuromorphic Hardware

Liu

Hadaeghi

et al. 2019

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

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Analog, unclocked, spiking neuromorphic microchips open new perspectives for implantable or wearable biosensors and biocontrollers, due to their low energy consumption and heat dissipation. However, the challenges from a computational point of view are formidable. Here we outline our solutions to realize the reservoir computing paradigm on such hardware and address the combined problems of low bit resolution, device mismatch, approximate neuron models, and timescale mismatch. The main contribution is a computational scheme, called Reservoir Transfer, which enables us to transfer the dynamical properties of a well-performing neural network which has been optimized on a digital computer, onto neuromorphic hardware that displays the abovementioned problematic properties. Here we present a case study of implementing an ECG heartbeat abnormality detector to showcase the proposed method.

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Section: Introductionmentioning

confidence: 99%

Reservoir Transfer on Analog Neuromorphic Hardware

Liu

Hadaeghi

et al. 2019

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

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“…However, this comparison is imperfect since we need to account for the power needed in generating mode complex features like MFCC. Tsai et al ( 2017 ) has shown that the power required for MFCC feature extraction is 122 mW on FPGA based implementation and 62.3 mW on ARM based implementation for TIDIGITS dataset using a 32 ms frame size. This is significantly higher than that of feature extraction techniques described in this paper (Tables 2 , 3 ).…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Low-Complexity Real-Time Feature Extraction for Neuromorphic Speech Recognition

Acharya

Patil

et al. 2018

Front. Neurosci.

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This paper presents a real-time, low-complexity neuromorphic speech recognition system using a spiking silicon cochlea, a feature extraction module and a population encoding method based Neural Engineering Framework (NEF)/Extreme Learning Machine (ELM) classifier IC. Several feature extraction methods with varying memory and computational complexity are presented along with their corresponding classification accuracies. On the N-TIDIGITS18 dataset, we show that a fixed bin size based feature extraction method that votes across both time and spike count features can achieve an accuracy of 95% in software similar to previously report methods that use fixed number of bins per sample while using ~3× less energy and ~25× less memory for feature extraction (~1.5× less overall). Hardware measurements for the same topology show a slightly reduced accuracy of 94% that can be attributed to the extra correlations in hardware random weights. The hardware accuracy can be increased by further increasing the number of hidden nodes in ELM at the cost of memory and energy.

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“…High-tech juggernauts and research agencies have heavily invested in massively parallel application-specific integrated circuits (ASICs) for evaluating neural network models more efficiently, notably IBM [6], HP [19], Intel [10], Google [20,21], the Human Brain Project [22], and DARPA SyNAPSE [23]. Some of these architectures aim to be ML number crunchers [20,24], and others have enabled novel neuroscientific tools [25,26] and previously unforeseen low-power mobile applications [27].…”

Section: Introductionmentioning

confidence: 99%

Silicon Photonic Modulator Neuron

et al. 2019

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There has been a recently renewed interest in neuromorphic photonics, a field promising to access pivotal and unexplored regimes of machine intelligence. Progress has been made on isolated neurons and analog interconnects; nevertheless, this renewal has yet to produce a demonstration of a silicon photonic neuron capable of interacting with other like neurons. We report a modulator-class photonic neuron fabricated in a conventional silicon photonic process line. We demonstrate behaviors of transfer function configurability, fan-in, inhibition, time-resolved processing, and, crucially, autaptic cascadability -a sufficient set of behaviors for a device to act as a neuron participating in a network of like neurons. The silicon photonic modulator neuron constitutes the final piece needed to make photonic neural networks fully integrated on currently available silicon photonic platforms.In this work, we fabricate and demonstrate a silicon photonic modulator neuron. It consists of a balanced photodetector directly connected to a microring (MRR) modulator. We demonstrate that this device possesses the necessary capabilities of a network-compatible neuron: fan-in, high-gain optical-to-optical nonlinearity, and arXiv:1812.11898v1 [physics.app-ph]

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Always-On Speech Recognition Using TrueNorth, a Reconfigurable, Neurosynaptic Processor

Cited by 28 publications

References 33 publications

Reservoir Transfer on Analog Neuromorphic Hardware

Reservoir Transfer on Analog Neuromorphic Hardware

A Comparison of Low-Complexity Real-Time Feature Extraction for Neuromorphic Speech Recognition

Silicon Photonic Modulator Neuron

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