The Deep Learning Hardware Battle

There is an ongoing race among semiconductor companies, including the established market heavyweights and startups alike, to define the hardware platform that will run compute-intensive deep learning algorithms quickly and efficiently. Until now, NVIDIA has dominated the deep learning market with its graphics processor unit (GPU) chips, which bring massive parallelization, however field programmable gate arrays (FPGAs) and digital signal processors (DSPs) are starting to catch up. Deep learning is largely characterized by deep neural networks (DNNs) and convolutional neural networks (CNNs), which can become massively complex. Google's cat recognition neural network back had 1 billion connections using 16,000 processors. GPUs are known to achieve the best speed and throughput, around 100x faster compared to an FPGA, while FPGAs are known to have better power efficiency, around 50x better compared to a GPU.