Ephrem C. Wu

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: In this paper, an embodiment of an apparatus consisting of an interposer, a first die stack, a second die stack and a third die stack coupled to the interposers is described.

TL;DR: In this article, a stacked die assembly for an IC includes a first interposer; a second interposers; a first integrated circuit die, a second IC die, and a plurality of components.

TL;DR: A processing array designed to consistently achieve timing closure at 100% of the peak DSP clock rate with standard FPGA tools for matrix multiplication and convolution, the most compute-intensive operations of a convolutional neural network (CNN).