EngineeringComputer EngineeringComputer ScienceComputer ArchitectureField-programmable Gate ArraysMachine LearningAccelerator ArchitectureDeep LearningFpga ArchitectureHardware AccelerationLow-power Fpga DesignReconfigurable AcceleratorsAcceleratorsData AccessDeep Learning AccelerationDeep Learning AcceleratorsHigh PerformancePower ConsumptionPower-efficient Accelerator DesignPrediction
Recently, machine learning is widely used in applications and cloud services. And as the emerging field of machine learning, deep learning shows excellent ability in solving complex learning problems. To give users better experience, high performance implementations of deep learning applications seem very important. As a common means to accelerate algorithms, FPGA has high performance, low power consumption, small size and other characteristics. So we use FPGA to design a deep learning accelerator, the accelerator focuses on the implementation of the prediction process, data access optimization and pipeline structure. Compared with Core 2 CPU 2.3GHz, our accelerator can achieve promising result.
7
A Fast Learning Algorithm for Deep Belief Nets
Geoffrey E. Hinton, Simon Osindero, Yee‐Whye Teh · Neural Computation · 2006 · 16.4K citations
Tianshi Chen, Zidong Du, Ninghui Sun et al. · 2014 · 1.3K citations