FPGA Implementations of Neural Networks

Abstract

In this article, I would like to discuss the implementation and using of neural networks on an FPGA [1] based system and also the topology of neural network deployment on FPGA, as well as the advantages of using FPGA-based neural networks over a CPU-based neural network. We will also discuss the requirements for neural networks deployed in an embedded system. Concluding with the advantages and disadvantages of the neural networks produced by the FPGA manufacturers as well as the advantages of using HLS[2] (High Level Synthesis) technology. And why SoC together with FPGA makes an ideal technological solution for embedded systems. We will also discuss which high-level programming languages will make it easier to develop neural networks on FPGA. АННОТАЦИЯ В этой статье я хотел бы обсудить реализацию и использование нейронных сетей на базе ПЛИС, топологию нейронных сетей на базе ПЛИС, а также преимущества использования нейронных сетей на базе ПЛИС перед нейронными сетями на основе обычных процессоров. Мы также обсудим требования к нейронным сетям, развернутым на встраиваемых системах. В заключение мы рассмотрим преимущества и недостатки нейронных сетей, выпускаемых производителями ПЛИС, а также преимущества использования технологии HLS (High Level Synthesis). Почему SoC вместе с ПЛИС являются идеальным технологическим решением для встраиваемых систем, а также обсудим, какие языки программирования высокого уровня облегчат разработку нейронных сетей на ПЛИС.

Exploring the spatiotemporal variability of the snow water equivalent in a small boreal forest catchment through observation and modelling

“…They are data-driven non-linear models that can identify the relationship between the input variables and output variables of interest (Anctil, Filion, & Tournebize, 2009). In general, NNs consist of (a) a set of inputs, (b) a set of synaptic connections whose weights represent its applicability and (c) an activation function relating the input to the output of individual neurons (Omondi, Rajapakse, & Bajger, 2006). As NNs extract set of rules from training datasets and derive outputs by processing large number of neurons (Omondi et al, 2006), they work well even in the presence of noise or measurement errors (Govindaraju, 2000).…”

“…In general, NNs consist of (a) a set of inputs, (b) a set of synaptic connections whose weights represent its applicability and (c) an activation function relating the input to the output of individual neurons (Omondi, Rajapakse, & Bajger, 2006). As NNs extract set of rules from training datasets and derive outputs by processing large number of neurons (Omondi et al, 2006), they work well even in the presence of noise or measurement errors (Govindaraju, 2000). However, it is important to point out that NNs are black-box models often unable to provide physical basis of a system, especially since they are typically over-parameterized (Anctil et al, 2009).…”

Multilayer Perceptron Training Accelerator Using Systolic Array

“…MAPLE [3], which is an FPGA-based training accelerator, has been proposed to speed up both training and inference by accelerating the matrix product operations on an arithmetic unit basis. However, there are few studies specializing in architectures for the training process [4]- [6]. Although recently, a system using FPGA clusters was proposed [7], [8] to accelerate training, according to research [6], the bottleneck in designing a fast trainingaccelerator is the memory size.…”