Dilip Sequeira

TL;DR: This paper presents the benchmarking method for evaluating ML inference systems, MLPerf Inference, and prescribes a set of rules and best practices to ensure comparability across systems with wildly differing architectures.

TL;DR: MLPerf Inference as mentioned in this paper is a benchmarking method for evaluating ML inference systems with different architectures and architectures. And it is based on the first call for submissions garnered more than 600 reproducible inference-performance measurements from 14 organizations, representing over 30 systems that showcase a wide range of capabilities.

TL;DR: An efficient quasi-custom instruction set for physics processing unit (PPU) is enabled by balancing the dictates of a parallel arrangement of multiple, independent vector processors (5) and programming considerations.

TL;DR: In this paper, a linear complementarity problem solver is characterized by multiple execution units operating in parallel to implement a competent computational method adapted to resolve physics-based LCPs in real-time.

TL;DR: In this paper, a projected iterative descent method is used to resolve LCPs related to rigid body dynamics, such that animation of the rigid body dynamic on a display system can occur in real-time.