Matching Items (6)
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- All Subjects: Neural networks (Computer science)
- Creators: Cao, Yu
Description
ABSTRACT Developing new non-traditional device models is gaining popularity as the silicon-based electrical device approaches its limitation when it scales down. Membrane systems, also called P systems, are a new class of biological computation model inspired by the way cells process chemical signals. Spiking Neural P systems (SNP systems), a certain kind of membrane systems, is inspired by the way the neurons in brain interact using electrical spikes. Compared to the traditional Boolean logic, SNP systems not only perform similar functions but also provide a more promising solution for reliable computation. Two basic neuron types, Low Pass (LP) neurons and High Pass (HP) neurons, are introduced. These two basic types of neurons are capable to build an arbitrary SNP neuron. This leads to the conclusion that these two basic neuron types are Turing complete since SNP systems has been proved Turing complete. These two basic types of neurons are further used as the elements to construct general-purpose arithmetic circuits, such as adder, subtractor and comparator. In this thesis, erroneous behaviors of neurons are discussed. Transmission error (spike loss) is proved to be equivalent to threshold error, which makes threshold error discussion more universal. To improve the reliability, a new structure called motif is proposed. Compared to Triple Modular Redundancy improvement, motif design presents its efficiency and effectiveness in both single neuron and arithmetic circuit analysis. DRAM-based CMOS circuits are used to implement the two basic types of neurons. Functionality of basic type neurons is proved using the SPICE simulations. The motif improved adder and the comparator, as compared to conventional Boolean logic design, are much more reliable with lower leakage, and smaller silicon area. This leads to the conclusion that SNP system could provide a more promising solution for reliable computation than the conventional Boolean logic.
ContributorsAn, Pei (Author) / Cao, Yu (Thesis advisor) / Barnaby, Hugh (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2013
Description
The objective of this thesis was to compare various approaches for classification of the `good' and `bad' parts via non-destructive resonance testing methods by collecting and analyzing experimental data in the frequency and time domains. A Laser Scanning Vibrometer was employed to measure vibrations samples in order to determine the spectral characteristics such as natural frequencies and amplitudes. Statistical pattern recognition tools such as Hilbert Huang, Fisher's Discriminant, and Neural Network were used to identify and classify the unknown samples whether they are defective or not. In this work, a Finite Element Analysis software packages (ANSYS 13.0 and NASTRAN NX8.0) was used to obtain estimates of resonance frequencies in `good' and `bad' samples. Furthermore, a system identification approach was used to generate Auto-Regressive-Moving Average with exogenous component, Box-Jenkins, and Output Error models from experimental data that can be used for classification
ContributorsJameel, Osama (Author) / Redkar, Sangram (Thesis advisor) / Arizona State University (Publisher)
Created2013
Description
Built-in-Self-Test (BiST) for transmitters is a desirable choice since it eliminates the reliance on expensive instrumentation to do RF signal analysis. Existing on-chip resources, such as power or envelope detectors, or small additional circuitry can be used for BiST purposes. However, due to limited bandwidth, measurement of complex specifications, such as IQ imbalance, is challenging. In this work, a BiST technique to compute transmitter IQ imbalances using measurements out of a self-mixing envelope detector is proposed. Both the linear and non linear parameters of the RF transmitter path are extracted successfully. We first derive an analytical expression for the output signal. Using this expression, we devise test signals to isolate the effects of gain and phase imbalance, DC offsets, time skews and system nonlinearity from other parameters of the system. Once isolated, these parameters are calculated easily with a few mathematical operations. Simulations and hardware measurements show that the technique can provide accurate characterization of IQ imbalances. One of the glaring advantages of this method is that, the impairments are extracted from analyzing the response at baseband frequency and thereby eliminating the need of high frequency ATE (Automated Test Equipment).
ContributorsByregowda, Srinath (Author) / Ozev, Sule (Thesis advisor) / Cao, Yu (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2012
Description
The past decade has seen a tremendous surge in running machine learning (ML) functions on mobile devices, from mere novelty applications to now indispensable features for the next generation of devices.
While the mobile platform capabilities range widely, long battery life and reliability are common design concerns that are crucial to remain competitive.
Consequently, state-of-the-art mobile platforms have become highly heterogeneous by combining a powerful CPUs with GPUs to accelerate the computation of deep neural networks (DNNs), which are the most common structures to perform ML operations.
But traditional von Neumann architectures are not optimized for the high memory bandwidth and massively parallel computation demands required by DNNs.
Hence, propelling research into non-von Neumann architectures to support the demands of DNNs.
The re-imagining of computer architectures to perform efficient DNN computations requires focusing on the prohibitive demands presented by DNNs and alleviating them. The two central challenges for efficient computation are (1) large memory storage and movement due to weights of the DNN and (2) massively parallel multiplications to compute the DNN output.
Introducing sparsity into the DNNs, where certain percentage of either the weights or the outputs of the DNN are zero, greatly helps with both challenges. This along with algorithm-hardware co-design to compress the DNNs is demonstrated to provide efficient solutions to greatly reduce the power consumption of hardware that compute DNNs. Additionally, exploring emerging technologies such as non-volatile memories and 3-D stacking of silicon in conjunction with algorithm-hardware co-design architectures will pave the way for the next generation of mobile devices.
Towards the objectives stated above, our specific contributions include (a) an architecture based on resistive crosspoint array that can update all values stored and compute matrix vector multiplication in parallel within a single cycle, (b) a framework of training DNNs with a block-wise sparsity to drastically reduce memory storage and total number of computations required to compute the output of DNNs, (c) the exploration of hardware implementations of sparse DNNs and architectural guidelines to reduce power consumption for the implementations in monolithic 3D integrated circuits, and (d) a prototype chip in 65nm CMOS accelerator for long-short term memory networks trained with the proposed block-wise sparsity scheme.
While the mobile platform capabilities range widely, long battery life and reliability are common design concerns that are crucial to remain competitive.
Consequently, state-of-the-art mobile platforms have become highly heterogeneous by combining a powerful CPUs with GPUs to accelerate the computation of deep neural networks (DNNs), which are the most common structures to perform ML operations.
But traditional von Neumann architectures are not optimized for the high memory bandwidth and massively parallel computation demands required by DNNs.
Hence, propelling research into non-von Neumann architectures to support the demands of DNNs.
The re-imagining of computer architectures to perform efficient DNN computations requires focusing on the prohibitive demands presented by DNNs and alleviating them. The two central challenges for efficient computation are (1) large memory storage and movement due to weights of the DNN and (2) massively parallel multiplications to compute the DNN output.
Introducing sparsity into the DNNs, where certain percentage of either the weights or the outputs of the DNN are zero, greatly helps with both challenges. This along with algorithm-hardware co-design to compress the DNNs is demonstrated to provide efficient solutions to greatly reduce the power consumption of hardware that compute DNNs. Additionally, exploring emerging technologies such as non-volatile memories and 3-D stacking of silicon in conjunction with algorithm-hardware co-design architectures will pave the way for the next generation of mobile devices.
Towards the objectives stated above, our specific contributions include (a) an architecture based on resistive crosspoint array that can update all values stored and compute matrix vector multiplication in parallel within a single cycle, (b) a framework of training DNNs with a block-wise sparsity to drastically reduce memory storage and total number of computations required to compute the output of DNNs, (c) the exploration of hardware implementations of sparse DNNs and architectural guidelines to reduce power consumption for the implementations in monolithic 3D integrated circuits, and (d) a prototype chip in 65nm CMOS accelerator for long-short term memory networks trained with the proposed block-wise sparsity scheme.
ContributorsKadetotad, Deepak Vinayak (Author) / Seo, Jae-Sun (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Vrudhula, Sarma (Committee member) / Cao, Yu (Committee member) / Arizona State University (Publisher)
Created2019
Description
Due to high level of integration in RF System on Chip (SOC), the test access points are limited to the baseband and RF inputs/outputs of the system. This limited access poses a big challenge particularly for advanced RF architectures where calibration of internal parameters is necessary and ensure proper operation. Therefore low-overhead built-in Self-Test (BIST) solution for advanced RF transceiver is proposed. In this dissertation. Firstly, comprehensive BIST solution for RF polar transceivers using on-chip resources is presented. In the receiver, phase and gain mismatches degrade sensitivity and error vector magnitude (EVM). In the transmitter, delay skew between the envelope and phase signals and the finite envelope bandwidth can create intermodulation distortion (IMD) that leads to violation of spectral mask requirements. Characterization and calibration of these parameters with analytical model would reduce the test time and cost considerably. Hence, a technique to measure and calibrate impairments of the polar transceiver in the loop-back mode is proposed.
Secondly, robust amplitude measurement technique for RF BIST application and BIST circuits for loop-back connection are discussed. Test techniques using analytical model are explained and BIST circuits are introduced.
Next, a self-compensating built-in self-test solution for RF Phased Array Mismatch is proposed. In the proposed method, a sinusoidal test signal with unknown amplitude is applied to the inputs of two adjacent phased array elements and measure the baseband output signal after down-conversion. Mathematical modeling of the circuit impairments and phased array behavior indicates that by using two distinct input amplitudes, both of which can remain unknown, it is possible to measure the important parameters of the phased array, such as gain and phase mismatch. In addition, proposed BIST system is designed and fabricated using IBM 180nm process and a prototype four-element phased-array PCB is also designed and fabricated for verifying the proposed method.
Finally, process independent gain measurement via BIST/DUT co-design is explained. Design methodology how to reduce performance impact significantly is discussed.
Simulation and hardware measurements results for the proposed techniques show that the proposed technique can characterize the targeted impairments accurately.
Secondly, robust amplitude measurement technique for RF BIST application and BIST circuits for loop-back connection are discussed. Test techniques using analytical model are explained and BIST circuits are introduced.
Next, a self-compensating built-in self-test solution for RF Phased Array Mismatch is proposed. In the proposed method, a sinusoidal test signal with unknown amplitude is applied to the inputs of two adjacent phased array elements and measure the baseband output signal after down-conversion. Mathematical modeling of the circuit impairments and phased array behavior indicates that by using two distinct input amplitudes, both of which can remain unknown, it is possible to measure the important parameters of the phased array, such as gain and phase mismatch. In addition, proposed BIST system is designed and fabricated using IBM 180nm process and a prototype four-element phased-array PCB is also designed and fabricated for verifying the proposed method.
Finally, process independent gain measurement via BIST/DUT co-design is explained. Design methodology how to reduce performance impact significantly is discussed.
Simulation and hardware measurements results for the proposed techniques show that the proposed technique can characterize the targeted impairments accurately.
ContributorsJeong, Jae Woong (Author) / Ozev, Sule (Thesis advisor) / Kitchen, Jennifer (Committee member) / Cao, Yu (Committee member) / Ogras, Umit Y. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Speech recognition and keyword detection are becoming increasingly popular applications for mobile systems. While deep neural network (DNN) implementation of these systems have very good performance,
they have large memory and compute resource requirements, making their implementation on a mobile device quite challenging. In this thesis, techniques to reduce the memory and computation cost
of keyword detection and speech recognition networks (or DNNs) are presented.
The first technique is based on representing all weights and biases by a small number of bits and mapping all nodal computations into fixed-point ones with minimal degradation in the
accuracy. Experiments conducted on the Resource Management (RM) database show that for the keyword detection neural network, representing the weights by 5 bits results in a 6 fold reduction in memory compared to a floating point implementation with very little loss in performance. Similarly, for the speech recognition neural network, representing the weights by 6 bits results in a 5 fold reduction in memory while maintaining an error rate similar to a floating point implementation. Additional reduction in memory is achieved by a technique called weight pruning,
where the weights are classified as sensitive and insensitive and the sensitive weights are represented with higher precision. A combination of these two techniques helps reduce the memory
footprint by 81 - 84% for speech recognition and keyword detection networks respectively.
Further reduction in memory size is achieved by judiciously dropping connections for large blocks of weights. The corresponding technique, termed coarse-grain sparsification, introduces
hardware-aware sparsity during DNN training, which leads to efficient weight memory compression and significant reduction in the number of computations during classification without
loss of accuracy. Keyword detection and speech recognition DNNs trained with 75% of the weights dropped and classified with 5-6 bit weight precision effectively reduced the weight memory
requirement by ~95% compared to a fully-connected network with double precision, while showing similar performance in keyword detection accuracy and word error rate.
they have large memory and compute resource requirements, making their implementation on a mobile device quite challenging. In this thesis, techniques to reduce the memory and computation cost
of keyword detection and speech recognition networks (or DNNs) are presented.
The first technique is based on representing all weights and biases by a small number of bits and mapping all nodal computations into fixed-point ones with minimal degradation in the
accuracy. Experiments conducted on the Resource Management (RM) database show that for the keyword detection neural network, representing the weights by 5 bits results in a 6 fold reduction in memory compared to a floating point implementation with very little loss in performance. Similarly, for the speech recognition neural network, representing the weights by 6 bits results in a 5 fold reduction in memory while maintaining an error rate similar to a floating point implementation. Additional reduction in memory is achieved by a technique called weight pruning,
where the weights are classified as sensitive and insensitive and the sensitive weights are represented with higher precision. A combination of these two techniques helps reduce the memory
footprint by 81 - 84% for speech recognition and keyword detection networks respectively.
Further reduction in memory size is achieved by judiciously dropping connections for large blocks of weights. The corresponding technique, termed coarse-grain sparsification, introduces
hardware-aware sparsity during DNN training, which leads to efficient weight memory compression and significant reduction in the number of computations during classification without
loss of accuracy. Keyword detection and speech recognition DNNs trained with 75% of the weights dropped and classified with 5-6 bit weight precision effectively reduced the weight memory
requirement by ~95% compared to a fully-connected network with double precision, while showing similar performance in keyword detection accuracy and word error rate.
ContributorsArunachalam, Sairam (Author) / Chakrabarti, Chaitali (Thesis advisor) / Seo, Jae-Sun (Thesis advisor) / Cao, Yu (Committee member) / Arizona State University (Publisher)
Created2016