1. [Publications](/publications) 2. MAESTRO: A Data-Centric Approach to Understand Reuse, Performance, and Hardware Cost of DNN Mappings # MAESTRO: A Data-Centric Approach to Understand Reuse, Performance, and Hardware Cost of DNN Mappings ![](/sites/default/files/styles/wide/public/publications/MAESTRO2.PNG?itok=fzKcizqv) The efficiency of an accelerator depends on three factors -- mapping, deep neural network (DNN) layers, and hardware -- constructing extremely complicated design space of DNN accelerators. To demystify such complicated design space and guide the DNN accelerator design for better efficiency, we propose an analytical cost model, MAESTRO. MAESTRO receives DNN model description and hardware resources information as a list, and mapping described in a data-centric representation we propose as inputs. The data-centric representation consists of three directives that enable concise description of mappings in a compiler-friendly form. MAESTRO analyzes various forms of data reuse in an accelerator based on inputs quickly and generates more than 20 statistics including total latency, energy, throughput, etc., as outputs. MAESTRO's fast analysis enables various optimization tools for DNN accelerators such as hardware design exploration tool we present as an example. ## Authors Hyoukjun Kwon (Georgia Tech) Prasanth Chatarasi (Georgia Tech) Vivek Sarkar (Georgia Tech) Tushar Krishna (Georgia Tech) [Michael Pellauer](/person/michael-pellauer) [Angshuman Parashar](/person/angshuman-parashar) ## Publication Date Wednesday, April 22, 2020 ## Published in [IEEE Micro (Issue: Top Picks of the 2019 Computer Architecture Conferences)](https://ieeexplore.ieee.org/document/9076333) ## Research Area [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) [Computer Architecture](/research-area/computer-architecture) ## External Links [IEEE Digital Library](https://ieeexplore.ieee.org/document/9076333) ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .