Comparing Neural Accelerators & Neuromorphic Architectures The False Idol of Operations Article Swipe
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· 2020
· Open Access
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· DOI: https://doi.org/10.1145/3381755.3381759
· OA: W3035778328
Accompanying the advanced computing capabilities neural networks are enabling across a suite of application domains, there is a resurgence in interest in understanding what architectures can efficiently enable these advanced computational demands. Both neural accelerators and neuromorphic approaches are emerging at different scales, resource requirements, and enabling capabilities. Beyond the similarity of executing neural network workloads, these two paradigms exhibit significant differences. As processing, memory, and communication are the core tenets of computing, here we compare architectures of neural accelerators and neuromorphic in these terms. Specifically we show that operations alone are a lacking singular measure of performance due to contrasting computational goals. These differing computational paradigms, to maximize the amount of computations performed or to compute as needed, are analogous to maximin and minimax decision theory reasoning. The differing objectives make neural accelerator and neuromorphic architectural choices suited to enable different computational demands.
