Benchmarking Neuromorphic Computing: Devil Is in the Details

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Benchmarking systems that use different interfaces, encodings, technology, and approaches has emerged as a hot topic in neuromorphic engineering over the past few years.

www.eetasia.com, Dec. 15, 2021 – 

Anyone building a new technology understands that success partly depends on showing value added–demonstrating that your technology is better than your competitors'. Only in this way can innovators attract investors and satisfy managers. When you are making a smaller, faster, lighter, more efficient replacement for something that already exists, this is easy–at least in principle–but it's much more difficult when you are trying to create something genuinely new and different.

Neuromorphic computing is among the fields in which engineers are attempting something genuinely new, and the lack of easy comparisons between different systems–neuromorphic and otherwise–can be a problem.

Part of the issue has to do with the complexity of the field. Neuromorphic technology is brain-inspired, but as discussed in an earlier article,1 there are many ways to implement that inspiration at the hardware level: analog or digital, spikes or not, continuous or discrete time, virtual or direct connections between neurons.

There are also competing goals and emphases within groups. Some wish to simulate biology, some focus on energy efficiency, others want to simulate human-like intelligence, and still others simply seek practical solutions to everyday machine-learning problems.

How to benchmark systems that use different interfaces, encodings, technology, and approaches has emerged as a hot topic in neuromorphic engineering over the past few years. Going back to 2016, there have been many attempts to compare systems for different applications, or to run different algorithms, networks, or sets of algorithms/networks.

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