FPGA implementation of PSO algorithm and neural networks

This thesis describes the Field Programmable Gate Array (FPGA) implementations of two powerful techniques of Computational Intelligence (CI), the Particle Swarm Optimization algorithm (PSO) and the Neural Network (NN). Particle Swarm Optimization (PSO) is a popular population-based optimization algorithm. While PSO has been shown to perform well in a large variety of problems, PSO is typically implemented in software. Population-based optimization algorithms such as PSO are well suited for execution in parallel stages. This allows PSO to be implemented directly in hardware and achieve much faster execution times than possible in software. In this thesis, a pipelined architecture for hardware PSO implementation is presented. Benchmark functions solved by software and FPGA hardware PSO implementations are compared. NNs are inherently parallel, with each layer of neurons processing incoming data independently of each other. While general purpose processors have reached impressive processing speeds, they still cannot fully exploit this inherent parallelism due to their sequential architecture. In order to achieve the high neural network throughput needed for real-time applications, a custom hardware design is needed. In this thesis, a digital implementation of an NN is developed for FPGA implementation. The hardware PSO implementation is designed using only VHDL, while the NN hardware implementation is designed using Xilinx System Generator. Both designs are synthesized using Xilinx ISE and implemented on the Xilinx Virtex-II Pro FPGA Development Kit --Abstract, page iii

CAD Tools for Synthesis of Sleep Convention Logic

This dissertation proposes an automated flow for the Sleep Convention Logic (SCL) asynchronous design style. The proposed flow synthesizes synchronous RTL into an SCL netlist. The flow utilizes commercial design tools, while supplementing missing functionality using custom tools. A method for determining the performance bottleneck in an SCL design is proposed. A constraint-driven method to increase the performance of linear SCL pipelines is proposed. Several enhancements to SCL are proposed, including techniques to reduce the number of registers and total sleep capacitance in an SCL design

Time-Delay Neural Network Based Predictions of Elephant Distribution in a South African Game Reserve

A large portion of South Africa\u27s elephant population can be found on small wildlife reserves. When confined to enclosed reserves the elephant densities are much higher than observed in the wild. The large nutritional demands and destructive foraging behavior of elephants threaten rare species of vegetation. If conservation management is to protect threatened species of vegetation, knowing how long elephants will stay in one area of the reserve as well as which area they will move to next is essential. The goal of this study is to train an artificial neural network to predict an elephant herd\u27s next position in the Pongola Game Reserve. Accurate predictions would provide a useful tool in assessing future impact of elephant populations on different areas of the reserve. The particle swarm optimization (PSO) algorithm is used to adapt the weights of the neural network. Results are presented to show the effectiveness of TDNN-PSO for elephant distribution prediction

Recurrent Neural Network Based Predictions of Elephant Migration in a South African Game Reserve

A large portion of South Africa\u27\u27s elephant population can be found on small wildlife reserves. When confined to enclosed reserves the elephant densities are much higher than observed in the wild. The large nutritional demands and destructive foraging behavior of elephants threaten rare species of vegetation. If conservation management is to protect threatened species of vegetation, knowing how long elephants will stay in one area of the reserve as well as which area they will move to next is essential. The goal of this study is to train a recurrent neural network (RNN) to continuously predict an elephant herd\u27\u27s next position in the Pongola Game Reserve. Accurate predictions would provide a useful tool in assessing future impact of elephant populations on different areas of the reserve. The particle swarm optimization (PSO) algorithm is used to adapt the weights of the neural network. Results are presented to show the effectiveness of RNN-PSO for elephant migration prediction

DSP-Based PSO Implementation for Online Optimization of Power System Stabilizers

Real-time implementations of controllers require optimization algorithms which can be performed quickly. In this paper, a digital signal processor (DSP) implementation of particle swarm optimization (PSO) is presented. PSO is used to optimize the parameters of two stabilizers used in a power system. The controllers and PSO are both implemented on a single DSP in a hardware-in-loop configuration. Results showing the performance and feasibility for real-time implementations of PSO are presented

A Neural Network Based Approach to Elephant Migration Prediction in a South African Game Reserve

A large portion of South Africa’s elephant population can be found on small wildlife reserves. When confined to enclosed reserves the elephant densities are much higher than observed in the wild. The large nutritional demands and destructive foraging behavior of elephants threaten rare species of vegetation. If conservation management is to protect threatened species of vegetation, knowing how long elephants will stay in one area of the reserve as well as which area they will move to next is essential. The goal of this study is to train a neural network to predict an elephant herd’s next position in the Pongola Game Reserve. Accurate predictions would provide a useful tool in assessing future impact of elephant populations on different areas of the reserve. The particle swarm optimization (PSO) algorithm is used to adapt the weights of the neural network. Results are presented to show the effectiveness of TDNN-PSO for elephant migration prediction

Hardware Implementations of Swarming Intelligence -- A Survey

Swarming intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) agents interacting locally with their environment cause coherent global patterns to emerge. Because there are no central processing requirements, SI is ideal for parallelization, which lends it well to hardware implementations in many inexpensive processors. Several implementations in hardware that exploit this property for rapid calculation and inexpensive construction are highlighted in this paper to provide a good starting point for developing hardware platforms for SI algorithms

Recurrent Neural Network Based Predictions of Elephant Migration in a South African Game Reserve

A large portion of South Africa\u27\u27s elephant population can be found on small wildlife reserves. When confined to enclosed reserves the elephant densities are much higher than observed in the wild. The large nutritional demands and destructive foraging behavior of elephants threaten rare species of vegetation. If conservation management is to protect threatened species of vegetation, knowing how long elephants will stay in one area of the reserve as well as which area they will move to next is essential. The goal of this study is to train a recurrent neural network (RNN) to continuously predict an elephant herd\u27\u27s next position in the Pongola Game Reserve. Accurate predictions would provide a useful tool in assessing future impact of elephant populations on different areas of the reserve. The particle swarm optimization (PSO) algorithm is used to adapt the weights of the neural network. Results are presented to show the effectiveness of RNN-PSO for elephant migration prediction

PREDICTION OF ELEPHANT MOVEMENT IN A GAME RESERVE USING NEURAL NETWORKS

A large number of South Africa's elephants can be found on small wildlife reserves. The large nutritional demands and destructive foraging behavior of elephants can threaten rare species of vegetation. If conservation management is to protect threatened species of vegetation, knowing how long elephants will stay in one area of a reserve as well as which area they will move to next could be useful. The goal of this study was to train a recurrent neural network to predict an elephant herd's next position in the Pongola Game Reserve. Accurate predictions would provide a useful tool in assessing future impact of elephant populations on different areas of the reserve. Particle swarm optimization (PSO), PSO initialized backpropagation (PSO-BP) and PSO initialized backpropagation through time (PSO-BPTT) algorithms are used to adapt the recurrent neural network's weights. The effectiveness of PSO, PSO-BP and PSO-BPTT for training a recurrent neural network for elephant migration prediction is compared and PSO-BPTT produces the most accurate predictions at the expense of more computational cost.Backpropagation through time, elephant migration, neural networks, prediction, PSO