Design space exploration of Convolutional Neural Networks based on Evolutionary Algorithms

This paper proposes a framework for design space exploration ofConvolutional Neural Networks (CNNs) using Genetic Algorithms(GAs). CNNs have many hyperparameters that need to be tunedcarefully in order to achieve favorable results when used for imageclassification tasks or similar vision applications. Genetic Algorithmsare adopted to efficiently traverse the huge search spaceof CNNs hyperparameters, and generate the best architecture thatfits the given task. Some of the hyperparameters that were testedinclude the number of convolutional and fully connected layers, thenumber of filters for each convolutional layer, and the number ofnodes in the fully connected layers. The proposed approach wastested using MNIST dataset for handwritten digit classification andresults obtained indicate that the proposed approach is able to generatea CNN architecture with validation accuracy up to 96.66% onaverage

Advanced Search Techniques For Circuit Partitioning

. Most real world problems especially circuit layout and VLSI design are too complex for any single processing technique to solve in isolation. Stochastic, adaptive and local search approaches have strengths and weaknesses and should be viewed not as competing models but as complimentary ones. This paper describes the application of a combined Tabu Search [1] and Genetic Algorithm heuristic to guide an efficient interchange algorithm to explore and exploit the solution space of a hypergraph partitioning problem. Results obtained indicate, that the generated solutions and running time of this hybrid are superior to results obtained from a combined eigenvector and node interchange method [11]. 1. Introduction In the combinatorial sense, the layout problem is a constrained optimization problem. We are given a description of a circuit (usually called a netlist) which is a description of switching elements and their connecting wires. We seek an assignment of geometric coordinates of the ci..

An Efficient Solution to Circuit Partitioning Using Tabu Search and Genetic Algorithms

The modern philosophy for constructing fast, globally convergent algorithms is to combine a simple globally convergent algorithm with a fast, locally convergent algorithm to form a hybrid. The work in this paper involves exploring and implementing hybrid solutions to the problem of circuit partitioning in VLSI physical design. Results obtained indicate that the generated solutions and running time of a combined Tabu Search and Genetic Algorithm are superior to results obtained from Simulated Annealing and Iterative Improvement methods based on module interchange. 1 Introduction Circuit partitioning is the task of dividing a circuit into two or more smaller parts. Given a VLSI netlist, the circuit partitioning attempts to divide the circuit into k parts of specified sizes so as to minimize the number of signal nets that interconnect components in the k partitions. Partitioning can be used directly to divide a circuit into portions that are implemented on separate physical components, s..