If your model can fit within 10MB, and is based on CNNs, then Gyrfalcon’s [0] ‘compute in memory’ inference chips can in theory provide 3-4x higher TOPS/watt co

If your model can fit within 10MB, and is based on CNNs, then Gyrfalcon’s [0] ‘compute in memory’ inference chips can in theory provide 3-4x higher TOPS/watt compared to the above inference chips you’ve mentioned, with around 12.6 TOPS/watt for one of their latest chips [1]. Their chips already seem to be in some new devices [2] [3]. [0] https://www.gyrfalcontech.ai/ https://www.gyrfalcontech.ai/ [1] https://www.eetimes.com/gyrfalcon-unveils-fourth-ai-accelerator-chip/ https://www.eetimes.com/gyrfalcon-unveils-fourth-ai-accelera... [2] https://www.zdnet.com/article/ai-comes-to-the-edge-with-solidrun-and-gyrfalcons-ai-inference-server/ https://www.zdnet.com/article/ai-comes-to-the-edge-with-soli... [3] https://www.prnewswire.com/news-releases/lg-launched-phone-powered-by-gyrfalcon-technology-incs-ai-chip-300933178.html https://www.prnewswire.com/news-releases/lg-launched-phone-p...