Search results
Results From The WOW.Com Content Network
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 (AI) and machine learning applications, including artificial neural networks and computer vision.
Specialized computer hardware is often used to execute artificial intelligence (AI) programs faster, and with less energy, such as Lisp machines, neuromorphic engineering, event cameras, and physical neural networks. Since 2017, several consumer grade CPUs and SoCs have on-die NPUs. As of 2023, the market for AI hardware is dominated by GPUs. [1]
Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering [4] to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are ...
Huawei launched its Kirin 970 at IFA this year, calling it the first chipset with a dedicated neural processing unit (NPU). Then, Apple unveiled the A11 Bionic chip, which powers the iPhone 8, 8 ...
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 ...
Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. [2] Google began using TPUs internally in 2015, and in 2018 made them available for third-party use, both as part of its cloud infrastructure and by ...
Qualcomm announced Hexagon Vector Extensions (HVX). HVX is designed to allow significant compute workloads for advanced imaging and computer vision to be processed on the DSP instead of the CPU. [19] In March 2015 Qualcomm announced their Snapdragon Neural Processing Engine SDK which allow AI acceleration using the CPU, GPU and Hexagon DSP. [20]
In 2013, IBM developed Watson, a cognitive computer that uses neural networks and deep learning techniques. [5] The following year, it developed the 2014 TrueNorth microchip architecture [6] which is designed to be closer in structure to the human brain than the von Neumann architecture used in conventional computers. [1]