Whenever errors cannot be amended within the body of an article’s content, corrections are displayed clearly in the last paragraph of the news content. Rather than remove completely the content containing a mistake, we provide clarification and admittance of our mistakes to preserve transparency.Thank you to the speakers, attendees, sponsors, press, and volunteers. Tutorial 1: ML Performance and Real World Applications Press coverage w/ slides) Tutorials: Sunday, August 22nd, 2021 Time (PDT) HC33 Proceedings (~140MB) (Synopsys Keynote unavailable. Machine learning is a rich, varied, and rapidly evolving field.

This tutorial will explore the applications, performance characteristics, and key challenges of many different unique workloads across training and inference. Modern Neural Networks and their Computational Characteristics In particular, we will focus on hardware/software co-optimization for the industry-standard MLPerf™ benchmarks and selected applications and considerations at prominent cloud players. This talk will survey the computational characteristics of modern DNN workloads by taking a look at the characteristics and trends of the major application domains: computer vision, language, speech, recommendation. We will review the main categories of operations that occur in these networks, such as convolution variants, attention modules, recurrent cells, embeddings, normalizations, etc. We will also examine how the nature of the input data (regular grid, sequence, graph, unstructured) influences the DNN model architectures and their choice of operations. Finally, we will outline how the workload of a given network differs between its training and inference, due to changes in input characteristics, operation fusions, and workload-reduction techniques such as quantization and sparsity. The MLPerf Training and Inference Benchmarks have become the industry standard for measuring machine learning system performance (speed). We will describe the design choices in benchmarking machine learning performance, and how the MLPerf Training and Inference Benchmarks navigate those choices. We will walk through the submission and review process for the benchmarks, with the goal of enabling smooth submissions for potential submitters. We will review industry progress as shown by 2+ years of results on the benchmark suites.

Lastly, we will discuss ongoing work to improve the benchmark suites, and how new collaborators can become involved and make a field-wide impact.

Idm 6.29 crack activator moi nhat