The most part of the computing effort for deep learning inference is based on mathematical operations which can be mostly grouped into the four-part that are convolutions; activations; pooling; and normalization. These all four share a few characteristics that make them well suited for special-purpose hardware implementation: their memory access patterns are extremely predictable & they are readily parallelized. For designing a new custom hardware accelerators for deep learning is clearly popular, but achieving the state-of-the-art performance, and efficiency with a new design is a complex and challenging problem. In order to help developers to advance the adoption of efficient AI inferencing in custom hardware designs, in 2017 Nvidia opened the source for the hardware design of the NVIDIA Deep Learning Accelerator. NVIDIA Deep Learning Accelerator is both scalable and highly configurable; it consists of many great features like the modular design that maintains flexibility & simplifies integration and it also promotes standardized, open architecture to address the computational demands of inference.

The NVIDIA Deep Learning Accelerator (NVDLA) is a free and open architecture that promotes a standard way to design deep learning inference accelerators. With its modular architecture, NVDLA is scalable, highly configurable, and designed to simplify integration and portability. The hardware supports a wide range of IoT devices. Delivered as an open source project under the NVIDIA Open NVDLA License, all of the software, hardware, and documentation will be available on GitHub.