EMEA 2021 https://www.tinyml.org/event/emea-2021
ZigZag: An Architecture-Mapping Design Space Exploration (DSE) Framework for Deep Learning Accelerator
Linyan MEI, PhD Student, KU Leuven
Building efficient embedded deep learning systems requires a tight co-design between DNN algorithms, hardware, and algorithm-to-hardware mapping, a.k.a. dataflow. However, owing to the large joint design space, finding an optimal solution through physical implementation becomes infeasible.
This talk introduces ZigZag, a rapid DSE framework for DNN accelerator architecture and mapping.
ZigZag consists of three key components: 1) an analytical energy-performance-area Hardware Cost Estimator, 2) two Mapping Search Engines that support spatial and temporal even/uneven mapping search, and 3) an Architecture Generator that auto-explores the wide memory hierarchy design space. It takes in a DNN model topology, hardware constraints, and technology parameters and produces optimal hardware architectures, mappings, and corresponding hardware cost (energy, latency, area) estimates. ZigZag uses an enhanced nested-for-loop format as a uniform representation to integrate algorithm, accelerator, and algorithm-to-accelerator mapping descriptions.
ZigZag extends the common DSE frameworks with uneven mapping opportunities and smart mapping search strategies for accelerated search. Uneven mapping decouples the memory hierarchy and mappings (temporal / spatial) of the different operands (W/I/O), opening up a whole new space for DSE, and thus better design points are found.
This talk will describe ZigZag and show the benchmarking experiments against published works, an in-house accelerator, and existing DSE frameworks, together with three case studies, to demonstrate the reliability and capability of ZigZag. Up to 64% more energy-efficient solutions are found compared to other SotAs DSE frameworks, due to ZigZag’s uneven mapping capabilities.
The talk will end with the newest research outcomes of the ZigZag team at KU Leuven, such as applying ZigZag to analog-in-memory-computing (AiMC) architectures and the new fast-and-flexible temporal mapping search method – Loop Order based Memory Allocation (LOMA).
ZigZag is published on IEEE Transactions on Computers, 2021:
ZigZag is open-source on: https://github.com/ZigZag-Project/zigzag.