23 research outputs found

    Application specific instruction set processor design for embedded application using the coware tool

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    An Application Specific Instruction Set Processor (ASIP) is widely used as a System on a Chip(SoC) Component. ASIPs possess an instruction set which is tai-lored to benefit a specific application. Such specialization allows ASIPs to serve as an intermediate between two dominant processor design styles- ASICs which has high processing abilities at the cost of limited programmability and Programmable solu-tions such as FPGAs that provide programming exibility at the cost of less energy eficiency. In this dissertation the goal is to design ASIP, keeping in mind a temper-ature sensor system. The platform used for processor design is LISA 2.0 description language and processor designing environment from CoWare. Coware processor de-signer allows processor architecture to be defined at an abstract level and automatic generation of chain of software tools like assembler, linker and simulator for functional verification followed by RTL level description. RTL level description is used to gen-erate synthesized report of the design using RTL compiler and finally the layout is created using Cadence encounter

    ASIP design based on CORDIC algorithm using Xilinx and CoWare designer tools

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    Efficient generation of trigonometric as well as exponential functions without much increase in hardware complexity has always been a challenge, owing mainly to their importance and widespread use in Digital Signal Processing applications besides other areas. One such algorithm which is very much effective for the calculation of trigonometric, hyperbolic, exponential, linear and logarithmic functions is the CORDIC algorithm. The algorithm is very much hardware efficient because it omits the dependence on multipliers and is rather a combination of shift-add operations. Application Specific Instruction-set Processors (ASIPs) are a type of processor that serve as a compromise between General Purpose Processors (GPPs) and Single Purpose Processors (SPP). Their data-path can be optimized for a particular class of operations such as embedded control, Digital Signal Processing (DSP) applications etc. This project deals with the design of an ASIP based on the CORDIC algorithm using two very popular hardware designing tools, i.e , Xilinx Integrated Development Environment (IDE) from Xilinx corporations, Inc. and LISA 2.0 description language and processor designing environment from CoWare

    Custom-Instruction Synthesis for Extensible-Processor Platforms

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    A Probabilistic Approach for the System-Level Design of Multi-ASIP Platforms

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    A framework for automated and optimized ASIP implementation supporting multiple hardware description languages

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    Profiling-Based Hardware/Software Co-Exploration for the Design of Video Coding Architectures

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    CHIPS: Custom Hardware Instruction Processor Synthesis

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    Energy analysis and optimisation techniques for automatically synthesised coprocessors

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    The primary outcome of this research project is the development of a methodology enabling fast automated early-stage power and energy analysis of configurable processors for system-on-chip platforms. Such capability is essential to the process of selecting energy efficient processors during design-space exploration, when potential savings are highest. This has been achieved by developing dynamic and static energy consumption models for the constituent blocks within the processors. Several optimisations have been identified, specifically targeting the most significant blocks in terms of energy consumption. Instruction encoding mechanism reduces both the energy and area requirements of the instruction cache; modifications to the multiplier unit reduce energy consumption during inactive cycles. Both techniques are demonstrated to offer substantial energy savings. The aforementioned techniques have undergone detailed evaluation and, based on the positive outcomes obtained, have been incorporated into Cascade, a system-on-chip coprocessor synthesis tool developed by Critical Blue, to provide automated analysis and optimisation of processor energy requirements. This thesis details the process of identifying and examining each method, along with the results obtained. Finally, a case study demonstrates the benefits of the developed functionality, from the perspective of someone using Cascade to automate the creation of an energy-efficient configurable processor for system-on-chip platforms

    RISPP: A Run-time Adaptive Reconfigurable Embedded Processor

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    This Ph.D. thesis describes a new approach for adaptive processors using a reconfigurable fabric (embedded FPGA) to implement application-specific accelerators. A novel modular Special Instruction composition is presented along with a run-time system that exploits the provided adaptivity. The approach was simulated and prototyped using and FPGA. Comparisons with state-of-the-art appl.-specific and reconf. processors demonstrate significant improvements according the performance and efficiency

    Cost-Efficient Soft-Error Resiliency for ASIP-based Embedded Systems

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    Recent decades have witnessed the rapid growth of embedded systems. At present, embedded systems are widely applied in a broad range of critical applications including automotive electronics, telecommunication, healthcare, industrial electronics, consumer electronics military and aerospace. Human society will continue to be greatly transformed by the pervasive deployment of embedded systems. Consequently, substantial amount of efforts from both industry and academic communities have contributed to the research and development of embedded systems. Application-specific instruction-set processor (ASIP) is one of the key advances in embedded processor technology, and a crucial component in some embedded systems. Soft errors have been directly observed since the 1970s. As devices scale, the exponential increase in the integration of computing systems occurs, which leads to correspondingly decrease in the reliability of computing systems. Today, major research forums state that soft errors are one of the major design technology challenges at and beyond the 22 nm technology node. Therefore, a large number of soft-error solutions, including error detection and recovery, have been proposed from differing perspectives. Nonetheless, most of the existing solutions are designed for general or high-performance systems which are different to embedded systems. For embedded systems, the soft-error solutions must be cost-efficient, which requires the tailoring of the processor architecture with respect to the feature of the target application. This thesis embodies a series of explorations for cost-efficient soft-error solutions for ASIP-based embedded systems. In this exploration, five major solutions are proposed. The first proposed solution realizes checkpoint recovery in ASIPs. By generating customized instructions, ASIP-implemented checkpoint recovery can perform at a finer granularity than what was previously possible. The fault-free performance overhead of this solution is only 1.45% on average. The recovery delay is only 62 cycles at the worst case. The area and leakage power overheads are 44.4% and 45.6% on average. The second solution explores utilizing two primitive error recovery techniques jointly. This solution includes three application-specific optimization methodologies. This solution generates the optimized error-resilient ASIPs, based on the characteristics of primitive error recovery techniques, static reliability analysis and design constraints. The resultant ASIP can be configured to perform at runtime according to the optimized recovery scheme. This solution can strategically enhance cost-efficiency for error recovery. In order to guarantee cost-efficiency in unpredictable runtime situations, the third solution explores runtime adaptation for error recovery. This solution aims to budget and adapt the error recovery operations, so as to spend the resources intelligently and to tolerate adverse influences of runtime variations. The resultant ASIP can make runtime decisions to determine the activation of spatial and temporal redundancies, according to the runtime situations. At the best case, this solution can achieve almost 50x reliability gain over the state of the art solutions. Given the increasing demand for multi-core computing systems, the last two proposed solutions target error recovery in multi-core ASIPs. The first solution of these two explores ASIP-implemented fine-grained process migration. This solution is a key infrastructure, which allows cost-efficient task management, for realizing cost-efficient soft-error recovery in multi-core ASIPs. The average time cost is only 289 machine cycles to perform process migration. The last solution explores using dynamic and adaptive mapping to assign heterogeneous recovery operations to the tasks in the multi-core context. This solution allows each individual ASIP-based processing core to dynamically adapt its specific error recovery functionality according to the corresponding task's characteristics, in terms of soft error vulnerability and execution time deadline. This solution can significantly improve the reliability of the system by almost two times, with graceful constraint penalty, in comparison to the state-of-the-art counterparts
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