T
Tayler Hetherington
Researcher at University of British Columbia
Publications - 14
Citations - 1897
Tayler Hetherington is an academic researcher from University of British Columbia. The author has contributed to research in topics: Deep learning & Efficient energy use. The author has an hindex of 8, co-authored 14 publications receiving 1642 citations.
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Journal ArticleDOI
Cnvlutin: ineffectual-neuron-free deep neural network computing
Jorge Albericio,Patrick Judd,Tayler Hetherington,Tor M. Aamodt,Natalie Enright Jerger,Andreas Moshovos +5 more
TL;DR: Cnvolutin (CNV), a value-based approach to hardware acceleration that eliminates most of these ineffectual operations, improving performance and energy over a state-of-the-art accelerator with no accuracy loss.
Proceedings ArticleDOI
GPUWattch: enabling energy optimizations in GPGPUs
Jingwen Leng,Tayler Hetherington,Ahmed ElTantawy,Syed Zohaib Gilani,Nam Sung Kim,Tor M. Aamodt,Vijay Janapa Reddi +6 more
TL;DR: This work proposes a new GPGPU power model that is configurable, capable of cycle-level calculations, and carefully validated against real hardware measurements, and accurately tracks the power consumption trend over time.
Proceedings ArticleDOI
Stripes: bit-serial deep neural network computing
TL;DR: This work presents STR, a hardware accelerator that uses bit-serial computations to improve energy efficiency and performance and its area and power overhead are estimated at 5 percent and 12 percent respectively.
Proceedings ArticleDOI
Proteus: Exploiting Numerical Precision Variability in Deep Neural Networks
Patrick Judd,Jorge Albericio,Tayler Hetherington,Tor M. Aamodt,Natalie Enright Jerger,Andreas Moshovos +5 more
TL;DR: Proteus is a layered extension over existing DNN implementations that converts between the numerical representation used by the DNN execution engines and the shorter, layer-specific fixed-point representation used when reading and writing data values to memory be it on-chip buffers or off-chip memory.
Proceedings ArticleDOI
Characterizing and evaluating a key-value store application on heterogeneous CPU-GPU systems
TL;DR: This work explores the challenges in porting Memcached to OpenCL and provides a detailed analysis intomemcached's behavior on a GPU to better explain the performance results observed on physical hardware.