Embedded devices are typically resource-constrained, making it difficult to run AI algorithms on embedded platforms. The Codasip Application Engineering team looked at what could make it easier from a software and hardware point of view. They used the Codasip L31 RISC-V core and Codasip Studio to explore and customize the design.
The Codasip Application Engineering team used TensorFlow Lite for Microcontrollers (TFLite-Micro) as a dedicated AI framework and compared the performance of the Codasip L31 processor core with both standard and custom extensions. This case study highlighted the benefits of custom instructions for neural networks.