In this thesis, we provide solutions to two different bio-inspired algorithms. The first is enhancing the performance of bio-inspired test generation for circuits described in RTL Verilog, specifically for branch coverage. We seek to improve upon an existing framework, BEACON, in terms of performance. BEACON is an Ant Colony Optimization (ACO) based test generation framework. Similar to other ACO frameworks, BEACON also has a good scope in improving performance using parallel computing. We try to exploit the available parallelism using both multi-core Central Processing Units (CPUs) and Graphics Processing Units(GPUs). Using our new multithreaded approach we can reduce test generation time by a factor of 25 — compared to the original implementation for a wide variety of circuits. We also provide a 2-dimensional factoring method for BEACON to improve available parallelism to yield some additional speedup. The second bio-inspired algorithm we address is for Deep Neural Networks. With the increasing prevalence of Neural Nets in artificial intelligence and mission-critical applications such as self-driving cars, questions arise about its reliability and robustness. We have developed a test-generation based technique and metric to evaluate the robustness of a Neural Nets outputs based on its sensitivity to its inputs. This is done by generating inputs which the neural nets find difficult to classify but at the same time is relatively apparent to human perception. We measure the degree of difficulty for generating such inputs to calculate our metric. / MS / High-level Hardware Design Languages (HDLs) has allowed designers to implement complicated hardware designs with considerably lesser effort. Unfortunately, design verification for the same circuits has failed to scale gracefully in terms of time and effort. Not only has it become more difficult for formal methods due to exponential complexity from increasing path explosion, but concrete test generation frameworks also face new issues such as the increased requirement in the volume of simulations. The advent of parallel computing using General Purpose Graphics Processing Units (GPGPUs) has led to improved performance for various applications. We propose to leverage both the multi-core CPU and the GPGPU for RTL test generation. This is achieved by implementing a test generation framework that can utilize the SIMD type parallelism available in GPGPUs and task level parallelism available on CPUs. The speedup achieved is extracted from both the test generation framework itself and also from refactoring the hardware model for multi-threaded test generation. For this purpose, we translate the RTL Verilog to a C++ and a CUDA compilable program. Experimental results show that considerable speedup can be achieved for test generation without loss of coverage.
In recent years, machine learning and artificial intelligence have taken a substantial leap forward with the discovery of Deep Neural Networks(DNN). Unfortunately, apart from Accuracy and FTest numbers, there exist very few metrics to qualify a DNN. This becomes a reliability issue as DNNs are quite frequently used in safety-critical applications. It is difficult to interpret how the parameters of a trained DNN help store the knowledge from the training inputs. Therefore it is also difficult to infer whether a DNN has learned parameters which might cause an output neuron to misfire wrongly, a bug. An exhaustive search of the input space of the DNN is not only infeasible but is also misleading. Thus, in our work, we try to apply test generation techniques to generate new test inputs based on existing training and testing set to qualify the underlying robustness. Attempts to generate these inputs are guided only by the prediction probability values at the final output layer. We observe that depending on the amount of perturbation and time needed to generate these inputs we can differentiate between DNNs of varying quality.
|Date||05 October 2018|
|Creators||Varadarajan, Aravind Krishnan|
|Contributors||Electrical and Computer Engineering, Hsiao, Michael S., Patterson, Cameron D., Zeng, Haibo|
|Source Sets||Virginia Tech Theses and Dissertation|
|Rights||In Copyright, http://rightsstatements.org/vocab/InC/1.0/|
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