Compact neural network accelerator in CodAL – Case study

Embedded devices are typically resource-constrained, making it difficult to run AI algorithms on them. The Codasip Application Engineering team looked at what could make it easier from a software and hardware point of view. This case study highlights how they used the Codasip L31 RISC-V core and Codasip Studio to implement an efficient and compact AI accelerator tightly coupled with the CPU pipeline.