An AI accelerator is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence applications, especially artificial neural networks, machine vision and machine learning. Typical applications include algorithms for robotics, internet of things and other data-intensive or sensor-driven tasks. They are often manycore designs and generally focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability. As of 2018[update], a typical AI integrated circuit chip contains billions of MOSFET transistors. A number of vendor-specific terms exist for devices in this category, and it is an emerging technology without a dominant design.
Computer systems have frequently complemented the CPU with special purpose accelerators for specialized tasks, known as coprocessors. Notable application-specific hardware units include video cards for graphics, sound cards, graphics processing units and digital signal processors. As deep learning and artificial intelligence workloads rose in prominence in the 2010s, specialized hardware units were developed or adapted from existing products to accelerate these tasks.
As early as 1993, digital signal processors were used as neural network accelerators e.g. to accelerate optical character recognition software. In the 1990s, there were also attempts to create parallel high-throughput systems for workstations aimed at various applications, including neural network simulations. FPGA-based accelerators were also first explored in the 1990s for both inference and training. ANNA was a neural net CMOS accelerator developed by Yann LeCun.
Heterogeneous computing refers to incorporating a number of specialized processors in a single system, or even a single chip, each optimized for a specific type of task. Architectures such as the Cell microprocessor have features significantly overlapping with AI accelerators including: support for packed low precision arithmetic, dataflow architecture, and prioritizing 'throughput' over latency. The Cell microprocessor was subsequently applied to a number of tasks including AI.
In the 2000s, CPUs also gained increasingly wide SIMD units, driven by video and gaming workloads; as well as support for packed low precision data types.. Due to increasing performance of CPUs, they are also being used for running AI workloads. CPUs are superior for DNNs with DNNs with small or medium-scale parallelism, for sparse DNNs and in low batch-size scenarios. In mobile-domain, the hardware and software stack of accelerators is not mature. Hence, a mobile-CPU may provide higher performance than a mobile-GPU. In data-centers supportin web services, CPUs allow meeting the variability in computing demand over time.
Use of GPU
Graphics processing units or GPUs are specialized hardware for the manipulation of images and calculation of local image properties. The mathematical basis of neural networks and image manipulation are similar, embarrassingly parallel tasks involving matrices, leading GPUs to become increasingly used for machine learning tasks. As of 2016[update], GPUs are popular for AI work, and they continue to evolve in a direction to facilitate deep learning, both for training and inference in devices such as self-driving cars. GPU developers such as Nvidia NVLink are developing additional connective capability for the kind of dataflow workloads AI benefits from. As GPUs have been increasingly applied to AI acceleration, GPU manufacturers have incorporated neural network specific hardware to further accelerate these tasks. Tensor cores are intended to speed up the training of neural networks.
Use of FPGAs
Deep learning frameworks are still evolving, making it hard to design custom hardware. Reconfigurable devices such as field-programmable gate arrays (FPGA) make it easier to evolve hardware, frameworks and software alongside each other.
Microsoft has used FPGA chips to accelerate inference. The application of FPGAs to AI acceleration motivated Intel to acquire Altera with the aim of integrating FPGAs in server CPUs, which would be capable of accelerating AI as well as general purpose tasks.
Emergence of dedicated AI accelerator ASICs
While GPUs and FPGAs perform far better[quantify] than CPUs for AI related tasks, a factor of up to 10 in efficiency may be gained with a more specific design, via an application-specific integrated circuit (ASIC). These accelerators employ strategies such as optimized memory use and the use of lower precision arithmetic to accelerate calculation and increase throughput of computation. Some adopted low-precision floating-point formats used AI acceleration are half-precision and the bfloat16 floating-point format. Companies such as Facebook, Amazon and Google are all designing their own AI ASICs.
In-memory computing architectures
This section needs expansion. You can help by adding to it. (October 2018)
In June 2017, IBM researchers announced an architecture in contrast to the Von Neumann architecture based on in-memory computing and phase-change memory arrays applied to temporal correlation detection, intending to generalize the approach to heterogeneous computing and massively parallel systems. In October 2018, IBM researchers announced an architecture based on in-memory processing and modeled on the human brain's synaptic network to accelerate deep neural networks. The system is based on phase-change memory arrays.
In-memory computing with analog resistive memories
In 2019 researchers from Politecnico di Milano found a way to solve systems of linear equations in a few tens of nanoseconds via a single operation. Their algorithm is based on in-memory computing with analog resistive memories which performs with high efficiencies of time and energy, via conducting matrix-vector multiplication in one step with Ohm’s law and Kirchhoff’s law. The researchers showed that a feedback circuit with cross-point resistive memories can solve algebraic problems such as systems of linear equations, matrix eigenvectors, and differential equations in just one step. Such an approach improves computational times drastically in comparison with conventional algorithms.
Atomically thin semiconductors
In 2020, Marega et al. published experiments with a large-area active channel material for developing logic-in-memory devices and circuits based on floating-gate field-effect transistors (FGFETs). Such atomically thin semiconductors are considered promising for energy-efficient machine learning applications, where the same basic device structure is used for both logic operations and data storage. The authors used two-dimensional materials such as semiconducting molybdenum disulphide.
As of 2016, the field is still in flux and vendors are pushing their own marketing term for what amounts to an "AI accelerator", in the hope that their designs and APIs will become the dominant design. There is no consensus on the boundary between these devices, nor the exact form they will take; however several examples clearly aim to fill this new space, with a fair amount of overlap in capabilities.
In the past when consumer graphics accelerators emerged, the industry eventually adopted Nvidia's self-assigned term, "the GPU", as the collective noun for "graphics accelerators", which had taken many forms before settling on an overall pipeline implementing a model presented by Direct3D.
- Autonomous vehicles: Nvidia has targeted their Drive PX-series boards at this space.
- Military robots
- Agricultural robots, for example pesticide-free weed control.
- Voice control, e.g. in mobile phones, a target for Qualcomm Zeroth.
- Machine translation
- Unmanned aerial vehicles, e.g. navigation systems, e.g. the Movidius Myriad 2 has been demonstrated successfully guiding autonomous drones.
- Industrial robots, increasing the range of tasks that can be automated, by adding adaptability to variable situations.
- Health care, to assist with diagnoses
- Search engines, increasing the energy efficiency of data centers and ability to use increasingly advanced queries.
- Natural language processing
- Cognitive computer
- Neuromorphic computing
- Physical neural network
- Optical neural network
- Deep learning accelerator
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