Volume 12, Number 19, November 2022

Screening Deep Learning Inference Accelerators at the Production Lines

  Authors

Ashish Sharma, Puneesh Khanna, Jaimin Maniyar, AI Group, Intel, India

  Abstract

Artificial Intelligence (AI) accelerators can be divided into two main buckets, one for training and another for inference over the trained models. Computation results of AI inference chipsets are expected to be deterministic for a given input. There are different compute engines on the Inference chip which help in acceleration of the Arithmetic operations. The Inference output results are compared with a golden reference output for the accuracy measurements. There can be many errors which can occur during the Inference execution. These errors could be due to the faulty hardware units and these units should be thoroughly screened in the assembly line before they are deployed by the customers in the data centre.

This paper talks about a generic Inference application that has been developed to execute inferences over multiple inputs for various real inference models and stress all the compute engines of the Inference chip. Inference outputs from a specific inference unit are stored and are assumed to be golden and further confirmed as golden statistically. Once the golden reference outputs are established, Inference application is deployed in the pre- and post-production environments to screen out defective units whose actual output do not match the reference. Strategy to compare against itself at mass scale resulted in achieving the Defects Per Million target for the customers.

  Keywords

Artificial Intelligence, Deep Learning, Inference, Neural Network Processor for Inference (NNP-I), ICE, DELPHI, DSP, SRAM, ICEBO, IFMs, OFMs, DPMO.