Shah N. Efficient Execution of Irregular Dataflow Graphs 2023

Textbook in PDF format

This book focuses on the acceleration of emerging irregular sparse workloads, posed by novel artificial intelligent (AI) models and sparse linear algebra. Specifically, the book outlines several co-optimized hardware-software solutions for a highly promising class of emerging sparse AI models called Probabilistic Circuit (PC) and a similar sparse matrix workload for triangular linear systems (SpTRSV). The authors describe optimizations for the entire stack, targeting applications, compilation, hardware architecture and silicon implementation, resulting in orders of magnitude higher performance and energy-efficiency compared to the existing state-of-the-art solutions. Thus, this book provides important building blocks for the upcoming generation of edge AI platforms.
Irregular Workloads at Risk of Losing the Hardware Lottery
Suitable Data Representation: A Study of Fixed-Point, Floating-Point, and PositTM Formats for Probabilistic AI
GraphOpt: Constrained-Optimization- Based Parallelization of Irregular Workloads for Multicore Processors
DAG Processing Unit Version 1 (DPU): Efficient Execution of Irregular Workloads on a Multicore Processor
DAG Processing Unit Version 2 (DPU-v2): Efficient Execution of Irregular Workloads on a Spatial Datapath
Conclusions and Future Work