173 citations · 782 across the 61 of their papers we have counts for
8 papers · 1 filter
LifeStream: A High-Performance Stream Processing Engine for Periodic Streams
Anand Jayarajan, Kimberly Hau, Andrew Goodwin +1
Hospitals around the world collect massive amounts of physiological data from their patients every day. Recently, there has been an increase in research interest to subject this da…
IOS: Inter-Operator Scheduler for CNN Acceleration
Yaoyao Ding, Ligeng Zhu, Zhihao Jia +2
To accelerate CNN inference, existing deep learning frameworks focus on optimizing intra-operator parallelization. However, a single operator can no longer fully utilize the availa…
FPRaker: A Processing Element For Accelerating Neural Network Training
Omar Mohamed Awad, Mostafa Mahmoud, Isak Edo +5
We present FPRaker, a processing element for composing training accelerators. FPRaker processes several floating-point multiply-accumulation operations concurrently and accumulates…
TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference
Mostafa Mahmoud, Isak Edo, Ali Hadi Zadeh +4
TensorDash is a hardware level technique for enabling data-parallel MAC units to take advantage of sparsity in their input operand streams. When used to compose a hardware accelera…
Skyline: Interactive In-Editor Computational Performance Profiling for Deep Neural Network Training
Geoffrey X. Yu, Tovi Grossman, Gennady Pekhimenko
Training a state-of-the-art deep neural network (DNN) is a computationally-expensive and time-consuming process, which incentivizes deep learning developers to debug their DNNs for…
Multi-node Bert-pretraining: Cost-efficient Approach
Jiahuang Lin, Xin Li, Gennady Pekhimenko
Recently, large scale Transformer-based language models such as BERT, GPT-2, and XLNet have brought about exciting leaps in state-of-the-art results for many Natural Language Proce…
Automatic Horizontal Fusion for GPU Kernels
Ao Li, Bojian Zheng, Gennady Pekhimenko +1
We present automatic horizontal fusion, a novel optimization technique that complements the standard kernel fusion techniques for GPU programs. Unlike the standard fusion, whose go…
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training
Hongyu Zhu, Amar Phanishayee, Gennady Pekhimenko
Modern deep neural network (DNN) training jobs use complex and heterogeneous software/hardware stacks. The efficacy of software-level optimizations can vary significantly when used…