DSPs Power Deep Learning SoCs

DSPs mark the third credible silicon choice for deep learning products, especially the embedded systems that require affordable and low-power solutions. Graphics processing units (or GPUs), and to some extent FPGAs, have generally been deployed for training models in deep learning neuron nets. And the computationally intensive evaluation and training process is often done offline in large server farms. Next up, these trained models are translated into the actual production environments using a hybrid of CPUs and GPUs, or a hybrid of CPUs and FPGAs. But what about embedded systems in automotive, consumer, and industrial environments that are highly sensitive to both cost and power consumption? Enter DSP-based system-on-chips (SoCs) that offer high-performance neural processing while providing more affordable low-power solutions in the embedded environment. Digital signal processors (or DSPs)…


Link to Full Article: DSPs Power Deep Learning SoCs