5 Parallel Prism: A topology for pipelined implementations of convolutional neural networks using computational memory

Understand

In-memory computing is an emerging computing paradigm that could enable deeplearning inference at significantly higher energy efficiency and reduced latency.

• The essential idea is to map the synaptic weights corresponding to each layer to one or more computational memory (CM) cores.
• During inference, these cores perform the associated matrix-vector multiply operations in place with O(1) time complexity, thus obviating the need to move the synaptic weights to an additional processing unit.
• Moreover, this architecture could enable the execution of these networks in a highly pipelined fashion.