Feature: Processors


Advances in parallel processing with neuromorphic analogue chip implementations


By Alexander Timofeev, CEO and Founder, Polyn Technology T


he shiſt from cloud to edge computing has changed the sensor node concept, from using a simple DSP near the sensor, to applying deep neural network


functionality at the sensor level. Hence, most recent trends in this field have targeted device energy optimisation and computing latency reduction.


Going parallel Traditional computers process tasks sequentially: once a task is completed, the next one begins. Although this method still serves many applications today, demands are calling for faster, more optimised and more energy-efficient computations, where parallel processing fits the bill. Computer scientists and engineers


approach parallel processing like the human brain, where data is distributed in parallel to all neurons of a layer, then instantly propagated to another layer, giving rise to neuromorphic


Neuromorphic analogue signal processing, or NASP, chip design embodies the


approach of a sparse neural network, with only the necessary connections


between neurons required for inference


computing. Tis makes neural networks computationally fast, allowing analysis and decisions to be as close to real time as possible. It also makes them much


18 July/August 2022 www.electronicsworld.co.uk


more energy-efficient than other types of processing, but, crucially, more robust, too. For example, an error in an algorithm code will most inevitably lead to a system error, whereas if an error appears in one or several connections in a neural network, this does not lead to failure – all connections are interdependent, work in parallel and are duplicated on multiple levels.


Neural network implementations A digital neural network is a model with simulated neurons, using standard step-by-step consecutive math operations in the digital processor core. Digital implementation is not well suited to neural network processing, since it cannot process the massively parallel data necessary for neural network computation. Digital neural networks can be implemented on traditional processors, but with lower efficiencies. Tere has been significant progress in neural network digital implementation


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