AI CHIPS
The most important step after building a model be it a linear regression, support vector classifier or a neural network( Convolutional Neural Network or Recurrent Neural Network ) is the training step.
The purpose of the training step is to learn the model parameters. For a linear regression model, the training finds the parameters θ so that the cost function.
Since the data set the model is going to be trained on is in general stored in a Pandas Data Frame containing rows and columns between 100 and 1000 and even more.
The training phase requires a lot of computations since we’re dealing with high dimensional matrices.
For the purpose of training there are many kinds of chips:
Central Processing Unit (CPU)
Graphic Processing Unit(GPU)
Field Programmable Gate Array(FPGA)
Huawei Ascend AI chips etc
CPU
The central processing unit (CPU) is the standard processor used in many devices. Compared to FPGAs and GPUs, the architecture of CPUs has a limited number of cores optimized for sequential serial processing. The limited number of cores diminishes the effectiveness of a CPU processor to process the large amounts of data in parallel needed to properly run an AI algorithm. The architecture of FPGAs and GPUs is designed with the intensive parallel processing capabilities required for handling multiple tasks quickly and simultaneously. FPGA and GPU processors can execute an AI algorithm much more quickly than a CPU. This means that an AI application or neural network will learn and react several times faster on a FPGA or GPU compared to a CPU.CPUs do offer some initial pricing advantages. When training small neural networks with a limited dataset, a CPU can be used, but the trade-off will be time. The CPU-based system will run much more slowly than an FPGA or GPU-based system. Another benefit of the CPU-based application will be power consumption. Compared to a GPU configuration, the CPU will deliver better energy efficiency.
GPUs
Graphic processing units (GPUs) were originally developed for use in generating computer graphics, virtual reality training environments and video that rely on advanced computations and floating-point capabilities for drawing geometric objects, lighting and color depth. In order for artificial intelligence to be successful, it needs a lot of data to analyze and learn from. This requires substantial computing power to execute the AI algorithms and shift large amounts of data. GPUs can perform these operations because they are specifically designed to quickly process large amounts of data used in rendering video and graphics. Their strong computational abilities have helped to make them popular in machine learning and artificial intelligence applications.
GPUs are good for parallel processing, which is the computation of very large numbers of arithmetic operations in parallel. This delivers respectable acceleration in applications with repetitive workloads that are performed repeatedly in rapid succession. Pricing on GPUs can come in under competitive solutions, with the average graphics card having a five-year lifecycle
FPGAs
Field programmable gate arrays (FPGAs) are types of integrated circuits with programmable hardware fabric. This differs from graphics processing units (GPUs) and central processing units (CPUs) in that the function circuitry inside an FPGA processor is not hard etched. This enables an FPGA processor to be programmed and updated as needed. This also gives designers the ability to build a neural network from scratch and structure the FPGA to best meet their needs.
The reprogrammable, reconfigurable architecture of FPGAs delivers key benefits to the ever-changing AI landscape, allowing designers to quickly test new and updated algorithms quickly. This delivers strong competitive advantages in speeding time to market and cost savings by not requiring the development and release of new hardware.
Huawei Ascend AI chips
There are 2 models of Ascend: Ascend 310 and Ascend 910. Ascend 910 is the Most Powerful AI processor. It was launched in 2019.
Some key features of Ascend 910 are:
Da Vinci architecture
Max power 310 W
128-channel full HD video Decoder: H.264/265
Half-Precision (FP16): 256 TFLOPS
Integer precision (INT8): 512 TOPS
It’s used for Model Training
In a typical training session based on ResNet-50, the combination of Ascend 910 and MindSpore (Huawei Deep Learning framework) is about two times faster at training AI models than other mainstream training cards using TensorFlow.
To conclude, for better training performance, we can use Ascend 910 processor. It’s available on the Huawei AI Computing Platform called Atlas.
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