AI accelerator

As early as 1993, digital signal processors were used as neural network accelerators e.g. to accelerate optical character recognition software.[7] In the 1990s, there were also attempts to create parallel high-throughput systems for workstations aimed at various applications, including neural network simulations.[8][9][10] FPGA-based accelerators were also first explored in the 1990s for both inference[11] and training.[12] ANNA was a neural net CMOS accelerator developed by Yann LeCun.[13]

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[14] 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[15][16][17] including AI.[18][19][20]

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.[21]

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.[22][23][24] As of 2016, GPUs are popular for AI work, and they continue to evolve in a direction to facilitate deep learning, both for training[25] and inference in devices such as self-driving cars.[26] GPU developers such as Nvidia NVLink are developing additional connective capability for the kind of dataflow workloads AI benefits from.[27] As GPUs have been increasingly applied to AI acceleration, GPU manufacturers have incorporated neural network specific hardware to further accelerate these tasks.[28][29] Tensor cores are intended to speed up the training of neural networks.[29]

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.[30][11][12][31]

Microsoft has used FPGA chips to accelerate inference.[32] 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.[33]

While GPUs and FPGAs perform far better than CPUs for AI related tasks, a factor of up to 10 in efficiency[34][35] 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.[36][37] Some adopted low-precision floating-point formats used AI acceleration are half-precision and the bfloat16 floating-point format.[38][39][40][41][42][43][44] Companies such as Facebook, Amazon and Google are all designing their own AI ASICs.[45][46]

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.[47] 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.[48] The system is based on phase-change memory arrays.[49]

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.[50]

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).[51] 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.[51]