Software engineers developing artificial intelligence (AI) models using standard frameworks such as Keras, PyTorch, and TensorFlow are usually not well-equipped to translate those models into silicon-based implementations. A new synthesizable tool claims to solve this design conundrum with faster and more power-efficient execution compared to standard AI processors.
