Reduced Operation Set Computing (ROSC) for Flexible, Future-Proof, High-Performance Inference

By _Imagination Blog

March 25, 2021

The versatility and power of deep learning means that modern neural networks have found myriad applications in areas as diverse as machine translation, action recognition, task planning, sentiment analysis and image processing. This is inevitable as the field matures, and there is a high and growing degree of specialisation, which is an accelerating trend. This makes it a challenge to keep with the current state of the art, let alone predict the future compute needs of neural networks.

Designers of neural network accelerator (NNA) IP have a Herculean task on their hands: making sure that their product is sufficiently general to apply to a very wide range of current and future applications, whilst guaranteeing high performance. In the mobile, automotive, data centre and embedded spaces targeted by Imagination’s cutting-edge IMG Series4 NNAs, there are even more stringent constraints on bandwidth, area and power consumption. The engineers at Imagination have found innovative ways to address these daunting challenges and deliver ultra-high-performance and future-proof IP.

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