124 citations · 311 across the 19 of their papers we have counts for
19 papers
Tensor Algebra Processing Primitives (TAPP): Towards a Standard for Tensor Operations
Jan Brandejs, Niklas Hörnblad, Edward F. Valeev +4
To address the absence of a universal standard interface for tensor operations, we introduce the Tensor Algebra Processing Primitives (TAPP), a C-based interface designed to decoup…
Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures
Evangelos Georganas, Dhiraj Kalamkar, Kirill Voronin +5
During the past decade, Deep Learning (DL) algorithms, programming systems and hardware have converged with the High Performance Computing (HPC) counterparts. Nevertheless, the pro…
FP8 Formats for Deep Learning
Paulius Micikevicius, Dusan Stosic, Neil Burgess +12
FP8 is a natural progression for accelerating deep learning training inference beyond the 16-bit formats common in modern processors. In this paper we propose an 8-bit floating poi…
FPGA-based AI Smart NICs for Scalable Distributed AI Training Systems
Rui Ma, Evangelos Georganas, Alexander Heinecke +2
Rapid advances in artificial intelligence (AI) technology have led to significant accuracy improvements in a myriad of application domains at the cost of larger and more compute-in…
Next-Generation Local Time Stepping for the ADER-DG Finite Element Method
Alexander Breuer, Alexander Heinecke
High-frequency ground motion simulations pose a grand challenge in computational seismology. Two main factors drive this challenge. First, to account for higher frequencies, we hav…
Efficient and Generic 1D Dilated Convolution Layer for Deep Learning
Narendra Chaudhary, Sanchit Misra, Dhiraj Kalamkar +5
Convolutional neural networks (CNNs) have found many applications in tasks involving two-dimensional (2D) data, such as image classification and image processing. Therefore, 2D con…