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Follow on Google News | Cortical.io Semantic Folding Approach demonstrates 2,800x acceleration and 4,300x increase in energSubstantial cost reduction for NLU implementations enables ubiquitous language intelligence
By: Cortical.io The goal of the benchmark was to compare the throughput performance of the classification- Staggering results were achieved by the simultaneous application of three distinct innovative steps:
Benchmark results BERT implemented in Python on an AMD Epyc Milan+NVIDA GPU Performance 0.18 MB / Sec Acceleration 1x Power consumption 2,260 mwh / MB Efficiency 1x Semantic Folding implemented in Java on an AMD Epyc Milan Performance 18.2 MB / Sec Acceleration 100x Power consumption 15 mwh / MB Efficiency 150x Semantic Folding implemented in binary on an AMD Epyc Milan+ 4 card Xilinx FPGA Performance 528.30 MB / Sec Acceleration 2856x Power consumption 0.46 mwh / MB Efficiency 4298x Benchmark results show that with Semantic Folding, the operations costs can be reduced from several dollars per classifier to a fraction of a cent, making large-scale classification use cases for the first time commercially viable. Example real world workloads could be hate-speech detection for nearly three billion Facebook users or content filtering the Twitter firehose for hundreds of millions of users. "Efficiency is the new precision in Artificial Intelligence," For more information, visit https://www.cortical.io. End
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