Matching Items (6)
Filtering by
- All Subjects: Computer architecture
- All Subjects: ASIC
- Creators: Vrudhula, Sarma
- Resource Type: Text
Description
In recent years we have witnessed a shift towards multi-processor system-on-chips (MPSoCs) to address the demands of embedded devices (such as cell phones, GPS devices, luxury car features, etc.). Highly optimized MPSoCs are well-suited to tackle the complex application demands desired by the end user customer. These MPSoCs incorporate a constellation of heterogeneous processing elements (PEs) (general purpose PEs and application-specific integrated circuits (ASICS)). A typical MPSoC will be composed of a application processor, such as an ARM Coretex-A9 with cache coherent memory hierarchy, and several application sub-systems. Each of these sub-systems are composed of highly optimized instruction processors, graphics/DSP processors, and custom hardware accelerators. Typically, these sub-systems utilize scratchpad memories (SPM) rather than support cache coherency. The overall architecture is an integration of the various sub-systems through a high bandwidth system-level interconnect (such as a Network-on-Chip (NoC)). The shift to MPSoCs has been fueled by three major factors: demand for high performance, the use of component libraries, and short design turn around time. As customers continue to desire more and more complex applications on their embedded devices the performance demand for these devices continues to increase. Designers have turned to using MPSoCs to address this demand. By using pre-made IP libraries designers can quickly piece together a MPSoC that will meet the application demands of the end user with minimal time spent designing new hardware. Additionally, the use of MPSoCs allows designers to generate new devices very quickly and thus reducing the time to market. In this work, a complete MPSoC synthesis design flow is presented. We first present a technique \cite{leary1_intro} to address the synthesis of the interconnect architecture (particularly Network-on-Chip (NoC)). We then address the synthesis of the memory architecture of a MPSoC sub-system \cite{leary2_intro}. Lastly, we present a co-synthesis technique to generate the functional and memory architectures simultaneously. The validity and quality of each synthesis technique is demonstrated through extensive experimentation.
ContributorsLeary, Glenn (Author) / Chatha, Karamvir S (Thesis advisor) / Vrudhula, Sarma (Committee member) / Shrivastava, Aviral (Committee member) / Beraha, Rudy (Committee member) / Arizona State University (Publisher)
Created2013
Description
Stream computing has emerged as an importantmodel of computation for embedded system applications particularly in the multimedia and network processing domains. In recent past several programming languages and embedded multi-core processors have been proposed for streaming applications. This thesis examines the execution and dynamic scheduling of stream programs on embedded multi-core processors. The thesis addresses the problem in the context of a multi-tasking environment with a time varying allocation of processing elements for a particular streaming application. As a solution the thesis proposes a two step approach where the stream program is compiled to gather key application information, and to generate re-targetable code. A light weight dynamic scheduler incorporates the second stage of the approach. The dynamic scheduler utilizes the static information and available resources to assign or partition the application across the multi-core architecture. The objective of the dynamic scheduler is to maximize the throughput of the application, and it is sensitive to the resource (processing elements, scratch-pad memory, DMA bandwidth) constraints imposed by the target architecture. We evaluate the proposed approach by compiling and scheduling benchmark stream programs on a representative embedded multi-core processor. We present experimental results that evaluate the quality of the solutions generated by the proposed approach by comparisons with existing techniques.
ContributorsLee, Haeseung (Author) / Chatha, Karamvir (Thesis advisor) / Vrudhula, Sarma (Committee member) / Chakrabarti, Chaitali (Committee member) / Wu, Carole-Jean (Committee member) / Arizona State University (Publisher)
Created2013
Description
Thanks to continuous technology scaling, intelligent, fast and smaller digital systems are now available at affordable costs. As a result, digital systems have found use in a wide range of application areas that were not even imagined before, including medical (e.g., MRI, remote or post-operative monitoring devices, etc.), automotive (e.g., adaptive cruise control, anti-lock brakes, etc.), security systems (e.g., residential security gateways, surveillance devices, etc.), and in- and out-of-body sensing (e.g., capsule swallowed by patients measuring digestive system pH, heart monitors, etc.). Such computing systems, which are completely embedded within the application, are called embedded systems, as opposed to general purpose computing systems. In the design of such embedded systems, power consumption and reliability are indispensable system requirements. In battery operated portable devices, the battery is the single largest factor contributing to device cost, weight, recharging time, frequency and ultimately its usability. For example, in the Apple iPhone 4 smart-phone, the battery is $40\%$ of the device weight, occupies $36\%$ of its volume and allows only $7$ hours (over 3G) of talk time. As embedded systems find use in a range of sensitive applications, from bio-medical applications to safety and security systems, the reliability of the computations performed becomes a crucial factor. At our current technology-node, portable embedded systems are prone to expect failures due to soft errors at the rate of once-per-year; but with aggressive technology scaling, the rate is predicted to increase exponentially to once-per-hour. Over the years, researchers have been successful in developing techniques, implemented at different layers of the design-spectrum, to improve system power efficiency and reliability. Among the layers of design abstraction, I observe that the interface between the compiler and processor micro-architecture possesses a unique potential for efficient design optimizations. A compiler designer is able to observe and analyze the application software at a finer granularity; while the processor architect analyzes the system output (power, performance, etc.) for each executed instruction. At the compiler micro-architecture interface, if the system knowledge at the two design layers can be integrated, design optimizations at the two layers can be modified to efficiently utilize available resources and thereby achieve appreciable system-level benefits. To this effect, the thesis statement is that, ``by merging system design information at the compiler and micro-architecture design layers, smart compilers can be developed, that achieve reliable and power-efficient embedded computing through: i) Pure compiler techniques, ii) Hybrid compiler micro-architecture techniques, and iii) Compiler-aware architectures''. In this dissertation demonstrates, through contributions in each of the three compiler-based techniques, the effectiveness of smart compilers in achieving power-efficiency and reliability in embedded systems.
ContributorsJeyapaul, Reiley (Author) / Shrivastava, Aviral (Thesis advisor) / Vrudhula, Sarma (Committee member) / Clark, Lawrence (Committee member) / Colbourn, Charles (Committee member) / Arizona State University (Publisher)
Created2012
Description
Improving energy efficiency has always been the prime objective of the custom and automated digital circuit design techniques. As a result, a multitude of methods to reduce power without sacrificing performance have been proposed. However, as the field of design automation has matured over the last few decades, there have been no new automated design techniques, that can provide considerable improvements in circuit power, leakage and area. Although emerging nano-devices are expected to replace the existing MOSFET devices, they are far from being as mature as semiconductor devices and their full potential and promises are many years away from being practical.
The research described in this dissertation consists of four main parts. First is a new circuit architecture of a differential threshold logic flipflop called PNAND. The PNAND gate is an edge-triggered multi-input sequential cell whose next state function is a threshold function of its inputs. Second a new approach, called hybridization, that replaces flipflops and parts of their logic cones with PNAND cells is described. The resulting \hybrid circuit, which consists of conventional logic cells and PNANDs, is shown to have significantly less power consumption, smaller area, less standby power and less power variation.
Third, a new architecture of a field programmable array, called field programmable threshold logic array (FPTLA), in which the standard lookup table (LUT) is replaced by a PNAND is described. The FPTLA is shown to have as much as 50% lower energy-delay product compared to conventional FPGA using well known FPGA modeling tool called VPR.
Fourth, a novel clock skewing technique that makes use of the completion detection feature of the differential mode flipflops is described. This clock skewing method improves the area and power of the ASIC circuits by increasing slack on timing paths. An additional advantage of this method is the elimination of hold time violation on given short paths.
Several circuit design methodologies such as retiming and asynchronous circuit design can use the proposed threshold logic gate effectively. Therefore, the use of threshold logic flipflops in conventional design methodologies opens new avenues of research towards more energy-efficient circuits.
The research described in this dissertation consists of four main parts. First is a new circuit architecture of a differential threshold logic flipflop called PNAND. The PNAND gate is an edge-triggered multi-input sequential cell whose next state function is a threshold function of its inputs. Second a new approach, called hybridization, that replaces flipflops and parts of their logic cones with PNAND cells is described. The resulting \hybrid circuit, which consists of conventional logic cells and PNANDs, is shown to have significantly less power consumption, smaller area, less standby power and less power variation.
Third, a new architecture of a field programmable array, called field programmable threshold logic array (FPTLA), in which the standard lookup table (LUT) is replaced by a PNAND is described. The FPTLA is shown to have as much as 50% lower energy-delay product compared to conventional FPGA using well known FPGA modeling tool called VPR.
Fourth, a novel clock skewing technique that makes use of the completion detection feature of the differential mode flipflops is described. This clock skewing method improves the area and power of the ASIC circuits by increasing slack on timing paths. An additional advantage of this method is the elimination of hold time violation on given short paths.
Several circuit design methodologies such as retiming and asynchronous circuit design can use the proposed threshold logic gate effectively. Therefore, the use of threshold logic flipflops in conventional design methodologies opens new avenues of research towards more energy-efficient circuits.
ContributorsKulkarni, Niranjan (Author) / Vrudhula, Sarma (Thesis advisor) / Colbourn, Charles (Committee member) / Seo, Jae-Sun (Committee member) / Yu, Shimeng (Committee member) / Arizona State University (Publisher)
Created2015
Description
While machine/deep learning algorithms have been successfully used in many practical applications including object detection and image/video classification, accurate, fast, and low-power hardware implementations of such algorithms are still a challenging task, especially for mobile systems such as Internet of Things, autonomous vehicles, and smart drones.
This work presents an energy-efficient programmable application-specific integrated circuit (ASIC) accelerator for object detection. The proposed ASIC supports multi-class (face/traffic sign/car license plate/pedestrian), many-object (up to 50) in one image with different sizes (6 down-/11 up-scaling), and high accuracy (87% for face detection datasets). The proposed accelerator is composed of an integral channel detector with 2,000 classifiers for five rigid boosted templates to make a strong object detection. By jointly optimizing the algorithm and efficient hardware architecture, the prototype chip implemented in 65nm demonstrates real-time object detection of 20-50 frames/s with 22.5-181.7mW (0.54-1.75nJ/pixel) at 0.58-1.1V supply.
In this work, to reduce computation without accuracy degradation, an energy-efficient deep convolutional neural network (DCNN) accelerator is proposed based on a novel conditional computing scheme and integrates convolution with subsequent max-pooling operations. This way, the total number of bit-wise convolutions could be reduced by ~2x, without affecting the output feature values. This work also has been developing an optimized dataflow that exploits sparsity, maximizes data re-use and minimizes off-chip memory access, which can improve upon existing hardware works. The total off-chip memory access can be saved by 2.12x. Preliminary results of the proposed DCNN accelerator achieved a peak 7.35 TOPS/W for VGG-16 by post-layout simulation results in 40nm.
A number of recent efforts have attempted to design custom inference engine based on various approaches, including the systolic architecture, near memory processing, and in-meomry computing concept. This work evaluates a comprehensive comparison of these various approaches in a unified framework. This work also presents the proposed energy-efficient in-memory computing accelerator for deep neural networks (DNNs) by integrating many instances of in-memory computing macros with an ensemble of peripheral digital circuits, which supports configurable multibit activations and large-scale DNNs seamlessly while substantially improving the chip-level energy-efficiency. Proposed accelerator is fully designed in 65nm, demonstrating ultralow energy consumption for DNNs.
This work presents an energy-efficient programmable application-specific integrated circuit (ASIC) accelerator for object detection. The proposed ASIC supports multi-class (face/traffic sign/car license plate/pedestrian), many-object (up to 50) in one image with different sizes (6 down-/11 up-scaling), and high accuracy (87% for face detection datasets). The proposed accelerator is composed of an integral channel detector with 2,000 classifiers for five rigid boosted templates to make a strong object detection. By jointly optimizing the algorithm and efficient hardware architecture, the prototype chip implemented in 65nm demonstrates real-time object detection of 20-50 frames/s with 22.5-181.7mW (0.54-1.75nJ/pixel) at 0.58-1.1V supply.
In this work, to reduce computation without accuracy degradation, an energy-efficient deep convolutional neural network (DCNN) accelerator is proposed based on a novel conditional computing scheme and integrates convolution with subsequent max-pooling operations. This way, the total number of bit-wise convolutions could be reduced by ~2x, without affecting the output feature values. This work also has been developing an optimized dataflow that exploits sparsity, maximizes data re-use and minimizes off-chip memory access, which can improve upon existing hardware works. The total off-chip memory access can be saved by 2.12x. Preliminary results of the proposed DCNN accelerator achieved a peak 7.35 TOPS/W for VGG-16 by post-layout simulation results in 40nm.
A number of recent efforts have attempted to design custom inference engine based on various approaches, including the systolic architecture, near memory processing, and in-meomry computing concept. This work evaluates a comprehensive comparison of these various approaches in a unified framework. This work also presents the proposed energy-efficient in-memory computing accelerator for deep neural networks (DNNs) by integrating many instances of in-memory computing macros with an ensemble of peripheral digital circuits, which supports configurable multibit activations and large-scale DNNs seamlessly while substantially improving the chip-level energy-efficiency. Proposed accelerator is fully designed in 65nm, demonstrating ultralow energy consumption for DNNs.
ContributorsKim, Minkyu (Author) / Seo, Jae-Sun (Thesis advisor) / Cao, Yu Kevin (Committee member) / Vrudhula, Sarma (Committee member) / Ogras, Umit Y. (Committee member) / Arizona State University (Publisher)
Created2019
Description
The holy grail of computer hardware across all market segments has been to sustain performance improvement at the same pace as silicon technology scales. As the technology scales and the size of transistors shrinks, the power consumption and energy usage per transistor decrease. On the other hand, the transistor density increases significantly by technology scaling. Due to technology factors, the reduction in power consumption per transistor is not sufficient to offset the increase in power consumption per unit area. Therefore, to improve performance, increasing energy-efficiency must be addressed at all design levels from circuit level to application and algorithm levels.
At architectural level, one promising approach is to populate the system with hardware accelerators each optimized for a specific task. One drawback of hardware accelerators is that they are not programmable. Therefore, their utilization can be low as they perform one specific function. Using software programmable accelerators is an alternative approach to achieve high energy-efficiency and programmability. Due to intrinsic characteristics of software accelerators, they can exploit both instruction level parallelism and data level parallelism.
Coarse-Grained Reconfigurable Architecture (CGRA) is a software programmable accelerator consists of a number of word-level functional units. Motivated by promising characteristics of software programmable accelerators, the potentials of CGRAs in future computing platforms is studied and an end-to-end CGRA research framework is developed. This framework consists of three different aspects: CGRA architectural design, integration in a computing system, and CGRA compiler. First, the design and implementation of a CGRA and its instruction set is presented. This design is then modeled in a cycle accurate system simulator. The simulation platform enables us to investigate several problems associated with a CGRA when it is deployed as an accelerator in a computing system. Next, the problem of mapping a compute intensive region of a program to CGRAs is formulated. From this formulation, several efficient algorithms are developed which effectively utilize CGRA scarce resources very well to minimize the running time of input applications. Finally, these mapping algorithms are integrated in a compiler framework to construct a compiler for CGRA
At architectural level, one promising approach is to populate the system with hardware accelerators each optimized for a specific task. One drawback of hardware accelerators is that they are not programmable. Therefore, their utilization can be low as they perform one specific function. Using software programmable accelerators is an alternative approach to achieve high energy-efficiency and programmability. Due to intrinsic characteristics of software accelerators, they can exploit both instruction level parallelism and data level parallelism.
Coarse-Grained Reconfigurable Architecture (CGRA) is a software programmable accelerator consists of a number of word-level functional units. Motivated by promising characteristics of software programmable accelerators, the potentials of CGRAs in future computing platforms is studied and an end-to-end CGRA research framework is developed. This framework consists of three different aspects: CGRA architectural design, integration in a computing system, and CGRA compiler. First, the design and implementation of a CGRA and its instruction set is presented. This design is then modeled in a cycle accurate system simulator. The simulation platform enables us to investigate several problems associated with a CGRA when it is deployed as an accelerator in a computing system. Next, the problem of mapping a compute intensive region of a program to CGRAs is formulated. From this formulation, several efficient algorithms are developed which effectively utilize CGRA scarce resources very well to minimize the running time of input applications. Finally, these mapping algorithms are integrated in a compiler framework to construct a compiler for CGRA
ContributorsHamzeh, Mahdi (Author) / Vrudhula, Sarma (Thesis advisor) / Gopalakrishnan, Kailash (Committee member) / Shrivastava, Aviral (Committee member) / Wu, Carole-Jean (Committee member) / Arizona State University (Publisher)
Created2015