Tensor processing unit

"We report a tensor processing unit (TPU) that is based on 3,000 carbon nanotube field-effect transistors and can perform energy-efficient convolution operations and matrix multiplication. 

"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 that offers a semiconductor purity of 99.9999% and ultraclean surfaces, leading to transistors with high on-current densities and uniformity. 

"Using system-level simulations, we estimate that an 8 bit TPU made with nanotube transistors at a 180 nm technology node could reach a main frequency of 850 MHz and an energy efficiency of 1 tera-operations per second per watt."



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