首页|Studies from Ain Shams University Have Provided New Data on Machine Learning (An Optimized Fpga Architecture for Machine Learning Applications)
Studies from Ain Shams University Have Provided New Data on Machine Learning (An Optimized Fpga Architecture for Machine Learning Applications)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Elsevier
Data detailed on Machine Learning have been presented. According to news reporting originating from Cairo, Egypt, by NewsRx correspondents, research stated, "FPGAs are currently the most suitable hardware accelerators to implement and accommodate the non-stop growth of machine learning applications. This paper presents an FPGA architecture with added posit multipliers that outweigh the current IEEE-754 multipliers in terms of delay and area." Our news editors obtained a quote from the research from Ain Shams University, "Since machine learning algorithms involve a lot of expensive mathematical operations, having such powerful multipliers in the proposed FPGA architecture will execute the needed operations with high efficiency, which will stand out for machine learning applications without compromising other FPGA applications. Experimental results using Verilog to Routing (VTR) on both machine learning and non-machine learning benchmarks have demonstrated that our proposed architecture consumes 10% less area than the Stratix Ⅳ FPGA. Furthermore, it consumes less power compared to both the Stratix Ⅳ and Stratix 10 FPGAs, with reductions of 22% and 10%, respectively."
CairoEgyptAfricaCyborgsEmerging TechnologiesMachine LearningAin Shams University
NETL
NSTL
Elsevier
