Trends in Machine Learning Hardware

Nov 10, 2023 ·

The article focuses on the trends in machine learning hardware. It looks at the evolution of hardware technology used for machine learning applications, from supercomputers and GPUs to FPGAs, ASICs, and neuromorphic chips. Supercomputers are still popular for some applications due to their high computational power, but GPU-based systems are becoming increasingly popular for training and inference due to their lower cost and greater scalability. FPGAs are becoming more widely used due to their flexibility and low-power consumption. ASICs (application specific integrated circuits) have become popular due to their optimized performance and lower cost compared to standard CPUs. Neuromorphic chips have recently gained traction due to their ability to emulate biological brains and produce highly efficient results.

In terms of hardware architecture, most systems are based on the von Neumann architecture which uses a control unit and memory components connected by a data bus. However, newer architectures such as the Heterogeneous Computing Architecture (HCA) or Systems-on-Chip (SoC) are being used to provide higher computational efficiency. These architectures combine multiple layers of processing elements, such as CPUs, GPUs, and FPGAs into a single chip. This allows developers to reduce costs and more efficiently utilize hardware resources.

In terms of programming languages, Python has been the predominant language for developing machine learning projects, however, new languages such as Julia, Scala, and Rust are gaining popularity as they offer better concurrency support and can be compiled to run faster on hardware. Additionally, there is a trend towards running machine learning models on edge devices, which requires software development kits (SDKs) to facilitate model deployment.

Overall, advancements in hardware technology are helping to make it easier and more cost-effective to develop and deploy machine learning models. There is a range of options available, with different levels of performance and cost, allowing developers to choose the best option for their application. The use of optimized hardware architectures, combined with the emergence of new programming languages, are making machine learning faster, easier, and cheaper.