• AxBench: A Benchmark Suite for Approximate Computing Across the System Stack 

      Yazdanbakhsh, Amir; Mahajan, Divya; Lotfi-Kamran, Pejman; Esmaeilzadeh, Hadi (Georgia Institute of Technology, 2016)
      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 ...
    • Methodical Approximate Hardware Design and Reuse 

      Yazdanbakhsh, Amir; Thwaites, Bradley; Park, Jongse; Esmaeilzadeh, Hadi (Georgia Institute of Technology, 2014)
      Design and reuse of approximate hardware components—digital circuits that may produce inaccurate results—can potentially lead to significant performance and energy improvements. Many emerging error-resilient applications ...
    • Neural Acceleration for GPU Throughput Processors 

      Yazdanbakhsh, Amir; Park, Jongse; Sharma, Hardik; Lotfi-Kamran, Pejman; Esmaeilzadeh, Hadi (Georgia Institute of Technology, 2015)
      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 ...
    • RFVP: Rollback-Free Value Prediction with Safe-to-Approximate Loads 

      Yazdanbakhsh, Amir; Pekhimenko, Gennady; Thwaites, Bradley; Esmaeilzadeh, Hadi; Kim, Taesoo; Mutlu, Onur; Mowry, Todd C. (Georgia Institute of Technology, 2015)
      This paper aims to tackle two fundamental memory bottle-necks: limited off-chip bandwidth (bandwidth wall) and long access latency (memory wall). To achieve this goal, our approach exploits the inherent error resilience ...
    • TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning 

      Mahajan, Divya; Park, Jongse; Amaro, Emmanuel; Sharma, Hardik; Yazdanbakhsh, Amir; Kim, Joon; Esmaeilzadeh, Hadi (Georgia Institute of Technology, 2015)
      A growing number of commercial and enterprise systems increasingly rely on compute-intensive machine learning algorithms. While the demand for these compute-intensive applications is growing, the performance benefits from ...
    • A Wireless Neural Recording SoC and Implantable Microsystem Integration 

      Duan, Lian; Wang, Tao; Wang, Siwei; Yazdanbakhsh, Amir (Georgia Institute of Technology, 2015)
      An integrated 4-channel wireless neural recording system architecture is proposed. The system was designed to detect extracellular activity potential in the brain. Highly power-efficient front-end signal processing, ...