
Myrtle.ai MAU Accelerator for AI Financial Trading Models
REFERENCE DESIGN MAU Accelerator for AI Financial Trading Models Ultra-low Latency, High Throughput Machine Learning Inference Well suited to a range of applications in financial
Working alongside CPUs, FPGAs provide part of a heterogeneous approach to computing. For certain workloads, FPGAs provide significant speedup versus CPU—in this case 50x faster for machine learning inference.
FPGAs have a range of tools to best tailor to the application. The hardware fabric adapts to use only what’s needed, including hardened floating-point blocks when required. For BWNN’s weights, we used only a single bit, plus mean scaling factor, and still achieved acceptable accuracy but saving significant resources.
Power per watt is not only important at the edge, it’s in the power budget of datacenters in both space and cost of power. FPGAs can uniquely deliver the latest efficient libraries yet at far lower power per watt than CPUs.
With BittWare’s exclusive optimized OpenCL BSP, you’re able to both tap into software-orientated developers and the latest software libraries. This allowed us to quickly adapt the YOLOv3 framework, which has improved performance over older ML libraries.
We target applications when demand to process storage outpaces traditional architectures featuring CPUs.
FPGAs allow customers to create application-specific hardware implementations that exhibit the following properties:
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REFERENCE DESIGN MAU Accelerator for AI Financial Trading Models Ultra-low Latency, High Throughput Machine Learning Inference Well suited to a range of applications in financial
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PCIe Gen4 data mover IP from Atomic Rules. Achieve up to 220 Gb/s using BittWare’s PCIe Gen4 cards, saving your development team when you need more performance than standard DMA. Features: DPDK and AXI standards, work with packets or any other data format, operate at any line rate up to 400 GbE.
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