#BENCHMARK AI OFFLINE#

MLPerf tests six common AIs under several conditions.

Neuchips only participated in the recommendation benchmark, as their accelerator, the RecAccel, is designed specifically to speed up recommender systems-which are used for recommending e-commerce items and for ranking search results. Intel used no accelerator chip at all, instead demonstrating the performance of its CPUs alone.

#BENCHMARK AI OFFLINE#

Computers meant to work onsite instead of in the data center-what MLPerf calls the edge-were measured in the offline state and as if they were receiving a single stream of data, such as from a security camera.Īlthough there were datacenter-class submissions from Dell, HPE, Inspur, Intel, LTech Korea, Lenovo, Nvidia, Neuchips, Qualcomm, and others, all but those from Qualcomm and Neuchips used Nvidia AI accelerator chips. For commercially available datacenter-based systems they were tested under two conditions-a simulation of real datacenter activity where queries arrive in bursts and 'offline' activity where all the data is available at once. In MLPerf's inferencing benchmarks, systems made up of combinations of CPUs and GPUs or other accelerator chips are tested on up to six neural networks performing a variety of common functions-image classification, object detection, speech recognition, 3D medical imaging, natural language processing, and recommendation.

The new results come on the heels of the announcement, last week, of a new machine-learning benchmark, called TCP-AIx. The majority of systems improved by between 5-30 percent from earlier this year, with some more than doubling their previous performance stats, according to MLCommons. The new list, MLPerf Version 1.1, follows the first official set of benchmarks by five months and includes more than 1800 results from 20 organizations, with 350 measurements of energy efficiency. This week, AI industry group MLCommons released a new set of results for AI performance.

Benchmark ai