Wave Computing has 30X faster deep learning training and 10-100X better performance

Wave Computing was founded with the vision of delivering deep learning computers with game-changing computational performance and energy efficiency. Their objective is to enable businesses to analyze complex data in real-time with more accurate results through a fluid discovery and improvement in Deep Neural Network (DNN) development and training with our family of computers.

Wave developed a novel Dataflow Processing Unit (DPU) architecture as part of a strategy to natively support a new wave of dataflow model based deep learning frameworks such as Google’s TensorFlow and Microsoft’s CNTK.

Wave’s family of deep learning computers achieves its best-in-class DNN training and inference performance through its native support of dataflow model based deep learning frameworks, its CPU-less high bandwidth shared memory architecture, and DPU’s 16,000+ parallel processing elements power and massive memory bandwidth. This results in a family of computers that delivers more than 10x improvement in compute performance for DNN training and more than 100x improvement in performance for DNN inference.

They have a custom chip for accelerating the TensorFlow algorithm Google developed and designed its own ASIC to run.

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