Naturally, ML practitioners started using GPUs to accelerate deep learning training and inference.ĬPU can offload complex machine learning operations to AI accelerators ( Illustration by author) GPUs were already in the market and over the years have become highly programmable unlike the early GPUs which were fixed function processors. The early 2010s saw yet another class of workloads - deep learning, or machine learning with deep neural networks - that needed hardware acceleration to be viable, much like computer graphics. You may know this special processor by its common name, the venerable GPU. Computer graphics was an early example of workload being offloaded to a special processor. This meant there was some need (and a market) for high-performance processors that could accelerate “special programs” much faster than a CPU, freeing up the CPU to do other things. Designers demanded better graphics, engineers and scientists demanded faster computers for data processing, modeling and simulations. The simpler system prevailed, and coprocessors and heterogeneous computing fell out of fashion for the regular user.Īround the same time specific types of workloads started to get more complex. Since the system had different types of processors (the CPU and the math coprocessor) the setup was sometimes referred to as heterogeneous computing.įast forward to the 90s, and the CPUs got faster, better and more efficient, and started to ship with integrated floating-point hardware. The idea was simple - allow the CPU to offload complex floating point mathematical operations to a specially designed chip, so that the CPU could focus on executing the rest of the application program, run the operating system etc. In the early days of computing (in the 70s and 80s), to speed up math computations on your computer, you paired a CPU (Central Processing Unit) with an FPU (floating-point unit) aka math coprocessor. In this blog post, I’ll explore three popular options: With great market needs comes great many product alternatives, so naturally there is more than one way to accelerate your ML models in the cloud. And you can speed up inference by offloading ML model prediction computation to an AI accelerator. A model that takes several hundreds of milliseconds to generate text translations or apply filters to images or generate product recommendations, can drive users away from your “sluggish”, “slow”, “frustrating to use” app.īy speeding up inference, you can reduce the overall application latency and deliver an app experience that can be described as “smooth”, “snappy”, and “delightful to use”. The prediction step (or inference) is often the most time consuming part of your application that directly affects user experience. Let’s say you have an ML model as part of your software application. Do I need an AI accelerator for machine learning (ML) inference? AI accelerators are specialized hardware designed to accelerate these basic machine learning computations and improve performance, reduce latency and reduce cost of deploying machine learning based applications. matrix-matrix, matrix-vector operations) and these operations can be easily parallelized. Machine learning, and particularly its subset, deep learning is primarily composed of a large number of linear algebra computations, (i.e. How to choose - GPUs, AWS Inferentia and Amazon Elastic Inference for inference ( Illustration by author) Let’s start by answering the question “What is an AI accelerator?”Īn AI accelerator is a dedicated processor designed to accelerate machine learning computations.
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