Edge Computing Vector Processor for CNN Applications

This work presents a vector processor architecture which can perform faster convolutions at the edge node compared to cloud computations. The proposed vector processor has an automated CNN Unit that performs convolution and max pooling functions. In addition to this, the processor is also capable of carrying out scalar and vector operations like addition and multiplication based on custom instructions. The processor can perform faster multiplications owing to a custom Dadda multiplier design which is 2.12 times faster than the traditional Dadda multiplier. The convolutor is parallelized and pipelined achieving a 5.405 speedup over the regular convolutor. The proposed processor is ultimately 97,888 times faster than Google Colab for convolution and max pooling.