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We demonstrate a low-power and compact hardware implementation of Random Feature Extractor (RFE) core. With complex tasks like Image Recognition requiring a large set of features, we show how weight reuse technique can allow to virtually expand the random features available from RFE core. Further, we show how to avoid computation cost wasted for propagating "incognizant" or redundant random features. For proof of concept, we validated our approach by using our RFE core as the first stage of Extreme Learning Machine (ELM)-a two layer neural network-and were able to achieve > 97% accuracy on MNIST database of handwritten digits. ELM's first stage of RFE is done on an analog ASIC occupying 5mm×5mm area in 0.35µm CMOS and consuming 5.95 µJ/classify while using ≈ 5000 effective hidden neurons. The ELM second stage consisting of just adders can be implemented as digital circuit with estimated power consumption of 20.9 nJ/classify. With a total energy consumption of only 5.97 µJ/classify, this low-power mixed signal ASIC can act as a co-processor in portable electronic gadgets with cameras.

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.