Multilayer perceptron neural networks have widely been implemented on reconfigurable hardware to perform a variety of applications including classification and pattern recognition. This paper investigates the combined impact of neural network size and reduced precision number formats, used for the representation of the optimal parameters, on the recognition rate a neural network based handwritten digit recognition system. The MNIST database is used for training and testing in this work. After deriving the optimal reduced-precision floating-point format sufficient for achieving a desired recognition performance, we provide an estimate for the hardware resources needed to implement the network on FPGAs. Our work allows for an efficient investigation of tradeoffs in operand word-length, network size, recognition rate and hardware cost of reduced-precision neural network implementations on reconfigurable hardware.
Convolutional neural network is now widely used in computer vision and deep learning applications. The most computeintensive layer in convolutional neural networks is the convolutional layer, which should be accelerated in hardware. This paper aims to develop an efficient hardware-software co-design framework for machine learning applications on the PYNQ-Z2 board. To achieve this goal, we develop hardware implementations of convolutional IP core and use them as Python overlays. Experiments show that the hardware implementations of the convolutional IP core outperform their software implementations by factors of up to 9 times. Furthermore, we make use of the designed convolutional IP core as hardware accelerator in the handwritten digit recognition application with MNIST dataset. Thanks to the use of the hardware accelerator for the convolutional layers, the execution performance of the convolutional neural network has been improved by a factor of 6.2 times.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.