With the proliferation of Artificial Intelligence and Machine Learning initiatives, we are witnessing a rapidly evolving demand for processors that can better accelerate neural network workloads. What is especially interesting about this developing technology is that we are seeing product coming to market from all types of processor manufacturers. Even more interesting is that each of these manufacturers offer something unique to the industry. The article that follows provides a list of the 10 best processors on the market right now that provide AI acceleration at the end point.
While the acceleration of AI and ML applications is still a relatively new field, there is a variety of processors springing up to accelerate almost any neural network workload. From the processor giants down to some of the newest startups in the industry, all offer something different — whether that’s targeting different vertical markets, application areas, power budgets, or price points. Here is a snapshot of what’s on the market today.
Application processors
Intel Movidius Myriad X
Developed by the Irish startup Movidius that was bought by Intel in 2016, the Myriad X is the company’s third-generation vision-processing unit and the first to feature a dedicated neural network compute engine, offering 1 tera operations per second (TOPS) of dedicated deep neural network (DNN) compute. The neural compute engine directly interfaces with a high-throughput intelligent memory fabric to avoid any memory bottleneck when transferring data. It supports FP16 and INT8 calculations. The Myriad X also features a cluster of 16 proprietary SHAVE cores and upgraded and expanded vision accelerators.
The Myriad X is available in Intel’s Neural Compute Stick 2, effectively an evaluation platform in the form of a USB thumb drive. It can be plugged into any workstation to allow AI and computer-vision applications to be up and running on the dedicated Movidius hardware very quickly.
