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School of Computer Science Technical Report Series

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Author: Esmaeilzadeh, Hadi × Author: Lotfi-Kamran, Pejman × Type: Text ×

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    AxBench: A Benchmark Suite for Approximate Computing Across the System Stack
    (Georgia Institute of Technology, 2016) Yazdanbakhsh, Amir ; Mahajan, Divya ; Lotfi-Kamran, Pejman ; Esmaeilzadeh, Hadi
    As the end of Dennard scaling looms, both the semiconductor industry and the research community are exploring for innovative solutions that allow energy efficiency and performance to continue to scale. Approximation computing has become one of the viable techniques to perpetuate the historical improvements in the computing landscape. As approximate computing attracts more attention in the community, having a general, diverse, and representative set of benchmarks to evaluate different approximation techniques becomes necessary. In this paper, we develop and introduce AxBench, a general, diverse and representative multi-framework set of benchmarks for CPUs, GPUs, and hardware design with the total number of 29 benchmarks. We judiciously select and develop each benchmark to cover a diverse set of domains such as machine learning, scientific computation, signal processing, image processing, robotics, and compression. AxBench comes with the necessary annotations to mark the approximable region of code and the application-specific quality metric to assess the output quality of each application. AxBenchwith these set of annotations facilitate the evaluation of different approximation techniques. To demonstrate its effectiveness, we evaluate three previously proposed approximation techniques using AxBench benchmarks: loop perforation [1] and neural processing units (NPUs) [2–4] on CPUs and GPUs, and Axilog [5] on dedicated hardware. We find that (1) NPUs offer higher performance and energy efficiency as compared to loop perforation on both CPUs and GPUs, (2) while NPUs provide considerable efficiency gains on CPUs, there still remains significant opportunity to be explored by other approximation techniques, (3) Unlike on CPUs, NPUs offer full benefits of approximate computations on GPUs, and (4) considerable opportunity remains to be explored by innovative approximate computation techniques at the hardware level after applying Axilog.
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    Neural Acceleration for GPU Throughput Processors
    (Georgia Institute of Technology, 2015) Yazdanbakhsh, Amir ; Park, Jongse ; Sharma, Hardik ; Lotfi-Kamran, Pejman ; Esmaeilzadeh, Hadi
    General-purpose computing on graphics processing units (GPGPU) accelerates the execution of diverse classes of applications, such as recognition, gaming, data analytics, weather prediction, and multimedia. Many of these applications are amenable to approximate execution. This application characteristic provides an opportunity to improve the performance and efficiency of GPGPU. Recent work has shown significant gains with neural approximate acceleration for CPU workloads. This work studies the effectiveness of neural approximate acceleration for GPU workloads. As applying CPU neural accelerators to GPUs leads to high area overhead, we define a low overhead neurally accelerated architecture for GPGPUs that enables scalable integration of neural acceleration on the large number of GPU cores. We also devise a mechanism that controls the tradeoff between the quality of results and the benefits from neural acceleration. We evaluate this design on a modern GPU architecture using a diverse set of benchmarks. Compared to the baseline GPGPU architecture, the cycle- accurate simulation results show 2.4 average speedup and 2.8 average energy reduction with 10% quality loss across all benchmarks. The quality control mechanism retains 1.9 average speedup and 2.1 energy reduction while reducing the quality degradation to 2.5%. These benefits are achieved by approximately 1.2% area overhead.