The lattice Boltzmann method (LBM) is regarded as a specific finite difference discretization
for the kinetic equation of the discrete velocity distribution function. We argue that for finite …

A multi-TOPS AI core is presented for acceleration of deep learning training and inference in
systems from edge devices to data centers. With a programmable architecture and custom …

The growing prevalence and computational demands of Artificial Intelligence (AI) workloads
has led to widespread use of hardware accelerators in their execution. Scaling the …

We argue on the basis of empirical data that Kolmogorov's refined similarity hypothesis (RSH)
needs to be modified for transverse velocity increments, and propose an alternative. In this …

Low-precision computation is the key enabling factor to achieve high compute densities (TOPS/W
and TOPS/mm 2 ) in AI hardware accelerators across cloud and edge platforms. …

Statistics and structures of pressure in three-dimensional incompressible isotropic turbulence
are studied using high-resolution direct numerical simulation for Taylor microscale …

Advances in deep neural networks (DNNs) and the availability of massive real-world data
have enabled superhuman levels of accuracy on many AI tasks and ushered the explosive …

A 1.35 ns random access and 1.7 ns-random-cycle SOI embedded-DRAM macro has been
developed for the POWER7™ high-performance microprocessor. The macro employs a 6 …

A processor core is presented for AI training and inference products. Leading-edge compute
efficiency is achieved for robust fp16 training via efficient heterogeneous 2-D systolic array-…

This letter presents a multi-TOPS AI accelerator core for deep learning training and inference.
With a programmable architecture and custom ISA, this engine achieves >90% sustained …