As DNNs become increasingly common in mission-critical applications, ensuring their
reliable operation has become crucial. Conventional resilience techniques fail to account for …

Faced with saturation of Moore's law and increasing size and dimension of data, system
designers have increasingly resorted to parallel and distributed computing to reduce …

We provide novel coded computation strategies for distributed matrix-matrix products that
outperform the recent “Polynomial code” constructions in recovery threshold, ie, the required …

We introduce the concept of “coded computing”, a novel computing paradigm that utilizes
coding theory to effectively inject and leverage data/computation redundancy to mitigate …

This paper has two main contributions. First, we propose a novel coding technique-
Generalized PolyDot-for matrix-vector products that advances on existing techniques for …

The half-precision (fp16) floating-point format, defined in the 2008 revision of the IEEE
standard for floating-point arithmetic, and a more recently proposed half-precision format …

Neural networks (NNs) are increasingly employed in safety-critical domains and in
environments prone to unreliability (eg, soft errors), such as on spacecraft. Therefore, it is …

Vision transformers (ViTs) that leverage self-attention mechanism have shown superior
performance on many classical vision tasks compared to convolutional neural networks …

Winograd is generally utilized to optimize convolution performance and computational
efficiency because of the reduced multiplication operations, but the reliability issues brought …

We propose a novel application of coded computing to the problem of the nearest neighbor
estimation using MatDot Codes (Fahim et al., Allerton'17) that are known to be optimal for …