Top 10 Processors for AI Acceleration at the Endpoint

(Source: EETIMES)

While the acceleration of artificial intelligence and machine learning 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 Movidius, the Irish startup 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.

NXP Semiconductors i.MX 8M Plus
The i.MX 8M Plus is a heterogeneous application processor featuring dedicated neural network accelerator IP from VeriSilicon (Vivante VIP8000). It offers 2.3 TOPS of acceleration for inference in endpoint devices in the consumer and industrial internet of things, enough for multiple object identification, speech recognition of 40,000 words, or even medical imaging (MobileNet v1 at 500 images per second).
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