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Survey of Bilinear Algorithms for Fast Matrix Multiplication – Part 1

January 17, 2021 in A.I Compiler Technologies 0 comments

Matrix Multiplication forms the foundation of Machine Learning. In this write up we take a survey of Bilinear Matrix Multiplication Algorithms that are the most common set of algorithms that perform better than the naive O(n^3) implementations. Short Form / Easy Read: Long Form: Though most of the time the […]

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Comparing Apple’s M1 matmul performance – AMX2 vs NEON

December 30, 2020 in A.I Compiler Technologies 8 comments

Matrix Multiply forms the foundation of Machine Learning computations. We show Apple’s M1 custom AMX2 Matrix Multiply unit can outperform ARMv8.6’s standard NEON instructions by about 2X. Nod’s AI Compiler team focusses on the state of art code generation, async partitioning, optimizations and scheduling to overlap communication and compute on […]

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Save 35% to 3X on your ML model training costs with Nod Runtime

December 19, 2020 in Distributed ML Runtime 0 comments

A.I Training doesn’t have to be expensive. Nod is opening up a limited early access program to select customers to deploy the industry’s most efficient and cost effective ML distributed training runtime. No cost to you if we don’t show a cost and / or efficiency improvement. Nod Runtime uses […]

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Optimized state of art monocular depth estimation on Xilinx DPUs

October 29, 2020 in Uncategorized 0 comments

Nod’s Runtime optimizations extend all the way to devices that consume a milliwatt for their CNN Inferencing. Nod recently showed off of its state of art Monocular Depth Estimation on the Xilinx DPU using Vitis-AI. Nod was able to optimize the model before targeting the Xilinx DPU to be able […]

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Nod.AI’s Neural Perception stack

February 22, 2020 in Uncategorized 0 comments

Computer Vision and Neural Perception have been disrupted with Machine Learning. Nod has optimized State of Art Computer Vision models deployed on really low power devices. Check out our post on LinkedIn

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