From algorithms to devices: Enabling machine learning through ultra-low-power VLSI mixed-signal array processing

2017 ◽  
Author(s):  
Siddharth Joshi ◽  
Chul Kim ◽  
Sohmyung Ha ◽  
Gert Cauwenberghs
Keyword(s):  
Machine Learning ◽  
Low Power ◽  
Array Processing ◽  
Ultra Low Power ◽  
Mixed Signal

An Ultra-Low Power Holter and Low Complexity Design Using Mixed Signal Processor

2013 ◽  
Vol 284-287 ◽  
pp. 1627-1632
Author(s):  
Hsieh Chang Huang ◽  
Ching Tang Hsieh ◽  
Guang Lin Hsieh
Keyword(s):  
Low Power ◽  
Analog Circuits ◽  
Finite Impulse Response ◽  
Low Complexity ◽  
Fir Filter ◽  
Ecg Signal ◽  
Ultra Low Power ◽  
Mixed Signal ◽  
Sd Card ◽  
Signal Processor

An ultra-low power, portable, and easily implemented Holter recorder is necessary for patients or researchers of electrocardiogram (ECG). Such a Holter recorder with off-the-shelf components is realized with mixed signal processor (MSP) in this paper. To decrease the complexity of analog circuits and the interference of 60 Hz noise from power line, we use the MSP to implement a finite impulse response (FIR) filter which is equiripple design. We also integrate the ring buffer for the input samples and the symmetrical characteristic of the FIR filter for efficiently computing convolution. The experimental results show that the ECG output signal with the PQRST feature is easy to be distinguished. This ECG signal is recorded for 24 hours using a SD card. Furthermore, the ECG signal is transmitted with a smartphone via Bluetooth to decrease the burden of the Holter recorder. As a result, this paper uses the Lomb method for the spectral analysis of Heart Rate Variability (HRV) better than Fast Fourier Transform (FFT).

scholarly journals Robust neuromorphic coupled oscillators for adaptive pacemakers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Renate Krause ◽  
Joanne J. A. van Bavel ◽  
Chenxi Wu ◽  
Marc A. Vos ◽  
Alain Nogaret ◽  
...  
Keyword(s):  
Neural Network ◽  
Low Power ◽  
Coupled Oscillators ◽  
Cardiac Pacemaker ◽  
Processing System ◽  
Spiking Neural Network ◽  
Ultra Low Power ◽  
Mixed Signal ◽  
Surface Ecg ◽  
Electronic Hardware

AbstractNeural coupled oscillators are a useful building block in numerous models and applications. They were analyzed extensively in theoretical studies and more recently in biologically realistic simulations of spiking neural networks. The advent of mixed-signal analog/digital neuromorphic electronic circuits provides new means for implementing neural coupled oscillators on compact, low-power, spiking neural network hardware platforms. However, their implementation on this noisy, low-precision and inhomogeneous computing substrate raises new challenges with regards to stability and controllability. In this work, we present a robust, spiking neural network model of neural coupled oscillators and validate it with an implementation on a mixed-signal neuromorphic processor. We demonstrate its robustness showing how to reliably control and modulate the oscillator’s frequency and phase shift, despite the variability of the silicon synapse and neuron properties. We show how this ultra-low power neural processing system can be used to build an adaptive cardiac pacemaker modulating the heart rate with respect to the respiration phases and compare it with surface ECG and respiratory signal recordings from dogs at rest. The implementation of our model in neuromorphic electronic hardware shows its robustness on a highly variable substrate and extends the toolbox for applications requiring rhythmic outputs such as pacemakers.

Towards on-node Machine Learning for Ultra-low-power Sensors Using Asynchronous Σ Δ Streams

2020 ◽  
Vol 16 (4) ◽  
pp. 1-20
Author(s):  
Patricia Gonzalez-Guerrero ◽  
Tommy Tracy II ◽  
Xinfei Guo ◽  
Rahul Sreekumar ◽  
Marzieh Lenjani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Low Power ◽  
Ultra Low Power
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