PRNewswire: Cadence unveils the Tensilica Vision C5 DSP, its first neural network DSP IP core for vision, radar/lidar and fused-sensor applications. Camera-based vision systems in automobiles, drones and security systems require two types of vision-optimized computation. First, the input from the camera is enhanced using traditional computational photography/imaging algorithms. Second, neural-network-based recognition algorithms perform object detection and recognition. Existing neural network accelerator solutions are hardware accelerators attached to imaging DSPs, with the neural network code split between running some network layers on the DSP and offloading convolutional layers to the accelerator. This combination is inefficient and consumes unnecessary power.
Architected as a dedicated neural-network-optimized DSP, the Vision C5 DSP accelerates all neural network computational layers (convolution, fully connected, pooling and normalization), not just the convolution functions. This frees up the main vision/imaging DSP to run image enhancement applications independently while the Vision C5 DSP runs inference tasks.
EETimes writes "The [Cadence Tensilica C5] cores are among as many as 50 silicon products now available to run various forms of computer vision and machine-learning tasks, said Jeff Bier, founder of the Embedded Vision Alliance, chairman of its event this week, and president of consulting firm BDTI. “There are so many [chips], with new ones popping up weekly, that it’s difficult to get a reliable count.”