Tensor Core & AI

The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) has necessitated the development of more powerful computational units to accommodate the increasing complexities of these fields. One key advancement in this arena is the invention of Tensor Cores, an essential component of NVIDIA’s Volta and newer GPU architectures. Tensor Cores are designed to accelerate matrix operations, which are at the heart of neural network computations, thereby providing monumental strides in AI development and inference.

Figure 1: Nvidia Tensor Core

Tensor Cores offer substantial benefits for AI research and development. Firstly, they significantly boost the computational efficiency, achieving far superior performance compared to traditional methods. They are optimized for performing mixed-precision matrix multiply and accumulate calculations, a common operation in AI applications, at a much faster rate than conventional GPU cores. This acceleration aids in training more complex, deeper neural networks in less time, enabling researchers and developers to innovate, experiment, and iterate rapidly.

Figure 2: Speed comparison between tensor core and non-tensor core in computation.

Secondly, Tensor Cores also enhance model accuracy. They support a mix of precisions that combines the benefits of FP16 (half-precision) compute for performance and TF32/FP32 (single-precision) for accuracy. This technique, known as mixed-precision computing, allows AI models to train faster while maintaining the accuracy of single-precision training. This balance between speed and precision leads to more robust, efficient models that can better generalize and make predictions, which is especially crucial in fields such as autonomous driving or medical diagnostics, where model accuracy can have life-altering implications.

Figure 3: Speed comparison between tensor core(V100) and non-tensor core(P100) in inference and training phrase.

Moreover, Tensor Cores provide significant benefits in terms of power efficiency since they shorten the training time and reduced the inference speed. They can perform more computations per unit of energy compared to traditional GPU cores. As AI models scale in size and computational demand, energy efficiency becomes increasingly important. Efficient use of power not only reduces operational costs but also contributes towards environmentally friendly AI development, a growing concern in the era of large-scale AI.

Figure 4: Energy consumption index from Data Centre throughout 2010-2019.

Lastly, Tensor Cores improve AI inference performance. As AI models become more complex, real-time inference becomes more computationally demanding. Tensor Cores, with their high throughput and efficiency, can handle these high computational loads, enabling faster, real-time responses. This speed-up is critical in many applications, from voice assistants that need to respond in real time, to autonomous vehicles that need to process vast amounts of data quickly to make instant decisions.

Figure 5: Inference speed of conversational AI application between each NVIDIA architecture.

In conclusion, the advent of Tensor Cores has been a game-changer in the field of AI, providing a significant boost in computational power, improving model accuracy, enhancing energy efficiency, and speeding up AI inference. They have fundamentally changed the landscape of AI development, opening the doors for more complex, effective, and efficient models that can drive advances across various sectors.

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