> The TPU is constructed with a systolic array architecture that allows parallel 2 bit integer multiply–accumulate operations. A five-layer convolutional neural network based on the TPU can perform MNIST image recognition with an accuracy of up to 88% for a power consumption of 295 µW. We use an optimized nanotube fabrication process [...] 1 TOPS/W/s
How do these fuzzy logic electronic components overcome the same challenges as analog electronic quantum computing?
The article describes performance on a visual CNN Convolutional Neural Network ML task.
Is quantum logic the only sufficient logic to describe systems with phase?
What is most production cost and operating cost efficient at this or similar ML tasks?
For reference, from https://news.ycombinator.com/item?id=41322088 re: "A carbon-nanotube-based tensor processing unit" (2024) https://www.nature.com/articles/s41928-024-01211-2 :
> The TPU is constructed with a systolic array architecture that allows parallel 2 bit integer multiply–accumulate operations. A five-layer convolutional neural network based on the TPU can perform MNIST image recognition with an accuracy of up to 88% for a power consumption of 295 µW. We use an optimized nanotube fabrication process [...] 1 TOPS/W/s