Efficient Execution of Irregular Dataflow Graphs

Chapter 1. Irregular workloads at risk of losing the hardware lottery.- Chapter 2. Suitable data representation: A study of fixed point, floating point,and positTM formats for probabilistic AI.- Chapter 3. GraphOpt: constrained-optimization-based parallelization of irregular workloads for multicore processors.- Chapter 4. DAG Processing Unit version 1 (DPU): Efficient execution of irregular workloads on a multicore processor.- Chapter 5. DAG Processing Unit version 2 (DPU-v2): Efficient execution of irregular workloads on a spatial datapath.- Chapter 6. Conclusions and future work.

Nimish Shah is an Electrical Engineering PhD student at KU Leuven, Belgium, advised by Prof. Marian Verhelst. His research focuses on hardware-software co-optimization for the energy-efficient acceleration of machine learning, sparse algebra, and graph workloads. Prior to debuting his doctoral studies, Mr. Shah was an ASIC engineer in a GPU design team at Nvidia, Bengaluru, India, working on data (de)compression hardware. He received an M.Tech in Electronic Systems Design from the Indian Institute of Science, and a B.Tech in Electronics Engineering from S.V. National Institute of Technology, Surat, India. Mr. Shah is a recipient of the HiSilicon Sponsorship Grant for MPW Prototyping, the ISSCC Code-a-Chip Travel Grant Award 2023, and the student travel grant for the IEEE/ACM MICRO 2022 conference. His research contributed to a joint project with Intel that won the "Intel 2021 Outstanding Researcher Award". He is also the recipient of the DESE Design Medal for excellence in his master's studies and thesis work at IISc, and was selected as a fellow for the undergraduate engineering stream under the prestigious Kishore Vaigyanik Protsahan Yojana (KVPY) 2010 of the Government of India.


Wannes Meert received his degrees of Master of Electrotechnical Engineering, Micro-electronics (2005), Master of Artificial Intelligence (2006) and Ph.D. in Computer Science (2011) from KU Leuven. He is an IOF research manager in the DTAI section at KU Leuven. His work is focused on applying machine learning, artificial intelligence and anomaly detection technology to industrial application domains with various industrial and academic partners..


Marian Verhelst is a full professor at the MICAS laboratories of KU Leuven and a research director at imec. Her research focuses on embedded machine learning, hardware accelerators, HW-algorithm co-design and low-power edge processing. She received a PhD from KU Leuven in 2008, and worked as a