Myrtle.ai, a recognized leader in accelerating machine learning inference, announced its VOLLO™ product has achieved the lowest latencies yet measured for the STAC-ML™ Markets (Inference) benchmarks. Created by the STAC Benchmark Council™, which includes the world’s largest global banks, brokerages, exchanges, hedge funds, proprietary trading shops, and asset managers, as well as more than 50 leading technology vendors, these benchmarks are a tool used to objectively compare different platforms for latency, throughput, efficiency and quality in machine learning (ML) inference.
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VOLLO achieved latencies as low as 24 microseconds with a throughput over 170k inferences/second1. The unrivaled latency and throughput enable users to make more intelligent decisions using more complex models faster than in the past, giving them a competitive advantage in trading, risk analysis, quotes and many other trading-related activities. Designed for simple and quick installation on co-located servers, VOLLO also enables reductions in rack space and energy consumption, two premium resources in such locations.
VOLLO runs on a standard form factor Intel FPGA-based PCIe accelerator card, up to 4 of which can be installed in a 1U server. VOLLO can be configured with customer-trained models, utilizing model architectures from the LSTM model zoo, enabling users to deploy a range of workloads specific to their application requirements via an export process from standard Machine Learning tool flows. It can support up to 12 parallel models per FPGA accelerator card installed in the system, enabling a maximum of 48 parallel models for a system with 4 cards installed.
A key enabling technology is the Intel Agilex F-Series FPGA. “Intel Agilex FPGAs are a superb delivery platform for our new fintech inference solution”, said Peter Baldwin, CEO of Myrtle.ai. “Agilex FPGAs have enabled us to quickly achieve great performance on these new machine learning benchmarks from STAC. Hardened bfloat16 support and sufficient on-chip RAM makes it ideal for accelerating the recurrent networks in the STAC benchmarks, wherever they are deployed, as those workloads evolve and scale”.
SOURCE: PR Newswire