Tachyum announced that it has successfully validated integer matrix operations running on its Prodigy Universal Processor FPGA hardware.

“Tachyum’s AI software stack supports AI applications with demanding computational requirements out of the box, rather than as an afterthought, which will radically change the efficiency and productivity of AI”

The Tachyum team tested and verified vector operations, 8-bit integer matrix operations for image classification using a Resnet model with custom convolution and linear operators on Prodigy.

AI Tech Insights: Clarifai Launches First Vendor-Agnostic Compute Orchestration Capabilities to Optimize AI Performance and Spend

Tachyum’s Prodigy was designed to handle matrix and vector processing from the ground up. Among Prodigy’s vector and matrix features are support for a range of data types (FP64, FP32, TF32, BF16, Int8, FP8, FP4 and TAI); 2×1024-bit vector units per core; AI sparsity and super-sparsity support; and no penalty for misaligned vector loads or stores when crossing cache lines. This built-in support offers high performance for AI training and inference workloads, increases performance, and reduces memory utilization.

“Tachyum’s AI software stack supports AI applications with demanding computational requirements out of the box, rather than as an afterthought, which will radically change the efficiency and productivity of AI,” said Dr. Radoslav Danilak, founder and CEO of Tachyum. “Data centers are among the most energy-hungry facilities in the world, and power infrastructures cannot accommodate the rapid adoption of AI without a platform like Prodigy that is capable of efficient AI processing, plus standard workloads.”

AI Tech Insights: Red Hat Partners with AWS to Drive Virtualization and AI Innovation in Hybrid Cloud

Tachyum will validate all supported data types. The next plan is to validate matrix operations for the FP8 data type this year on Prodigy FPGA hardware.

As a Universal Processor offering industry-leading performance for all workloads, Prodigy-powered data center servers can seamlessly and dynamically switch between computational domains (such as AI/ML, HPC, and cloud) with a single homogeneous architecture. By eliminating the need for expensive dedicated AI hardware and dramatically increasing server utilization, Prodigy reduces CAPEX and OPEX significantly while delivering unprecedented data center performance, power, and economics. Prodigy integrates 192 high-performance custom-designed 64-bit compute cores, to deliver up to 4.5x the performance of the highest-performing x86 processors for cloud workloads, up to 3x that of the highest performing GPU for HPC, and 6x for AI applications.

A video demonstration of 8-bit integer matrix operations running on Prodigy FPGA is available.

AI Tech Insights: Red Hat Buys Neural Magic: How This Move Will Transform the Future of AI

Source – businesswire

To share your insights, please write to us at news@intentamplify.com