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An AI accelerator, deep learning processor or neural processing unit (NPU) is a class of specialized hardware accelerator [1] or computer system [2] [3] designed to accelerate artificial intelligence and machine learning applications, including artificial neural networks and computer vision.
In 2021, Google revealed the physical layout of TPU v5 is being designed with the assistance of a novel application of deep reinforcement learning. [34] Google claims TPU v5 is nearly twice as fast as TPU v4, [ 35 ] and based on that and the relative performance of TPU v4 over A100, some speculate TPU v5 as being as fast as or faster than an H100 .
The NVIDIA Deep Learning Accelerator (NVDLA) is an open-source hardware neural network AI accelerator created by Nvidia. [1] The accelerator is written in Verilog and is configurable and scalable to meet many different architecture needs. NVDLA is merely an accelerator and any process must be scheduled and arbitered by an outside entity such as ...
OpenAI estimated the hardware compute used in the largest deep learning projects from Alex Net (2012) to Alpha Zero (2017), and found a 300,000-fold increase in the amount of compute needed, with a doubling-time trend of 3.4 months. [8] [9]
AMD Instinct is AMD's brand of data center GPUs. [1] [2] It replaced AMD's FirePro S brand in 2016.Compared to the Radeon brand of mainstream consumer/gamer products, the Instinct product line is intended to accelerate deep learning, artificial neural network, and high-performance computing/GPGPU applications.
The product line is intended to bridge the gap between GPUs and AI accelerators using specific features for deep learning workloads. [4] The initial Pascal-based DGX-1 delivered 170 teraflops of half precision processing, [5] while the Volta-based upgrade increased this to 960 teraflops. [6]