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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 (AI) and machine learning applications, including artificial neural networks and computer vision.
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 ...
Tensor cores: A tensor core is a unit that multiplies two 4×4 FP16 matrices, and then adds a third FP16 or FP32 matrix to the result by using fused multiply–add operations, and obtains an FP32 result that could be optionally demoted to an FP16 result. [12] Tensor cores are intended to speed up the training of neural networks. [12]
Third-generation Tensor Cores with FP16, bfloat16, TensorFloat-32 (TF32) and FP64 support and sparsity acceleration. [9] The individual Tensor cores have with 256 FP16 FMA operations per clock 4x processing power (GA100 only, 2x on GA10x) compared to previous Tensor Core generations; the Tensor Core Count is reduced to one per SM.
According to 2012 estimation, Qualcomm shipped 1.2 billion DSP cores inside its system on a chip (SoCs) (average 2.3 DSP core per SoC) in 2011, and 1.5 billion cores were planned for 2012, making the QDSP6 the most shipped architecture of DSP [12] (CEVA had around 1 billion of DSP cores shipped in 2011 with 90% of IP-licensable DSP market [13]).
Google Tensor is a series of ARM64-based system-on-chip (SoC) processors designed by Google for its Pixel devices. It was originally conceptualized in 2016, following the introduction of the first Pixel smartphone, though actual developmental work did not enter full swing until 2020.
The Apple A18 and A18 Pro feature an Apple-designed 64-bit ARMv9.2-A six-core CPU with two high-performance cores and four energy-efficient cores, a five-core (A18) and six-core (A18 Pro) GPU and a NPU with 16 cores. Both are produced on TSMC N3E (3nm FinFET) and measure 90 mm 2 and 105 mm 2 respectively. [6]
The Tensor cores perform the result of deep learning to codify how to, for example, increase the resolution of images generated by a specific application or game. In the Tensor cores' primary usage, a problem to be solved is analyzed on a supercomputer, which is taught by example what results are desired, and the supercomputer determines a ...