1. [Publications](/index.php/publications) 2. UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition # UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition ![](/sites/default/files/styles/wide/public/publications/UCNN.PNG?itok=F7_4kaJ1) Convolutional Neural Networks (CNNs) have begun to permeate all corners of electronic society (from voice recognition to scene generation) due to their high accuracy and machine efficiency per operation. At their core, CNN computations are made up of multi-dimensional dot products between weight and input vectors. This paper studies how weight repetition - when the same weight occurs multiple times in or across weight vectors - can be exploited to save energy and improve performance during CNN inference. This generalizes a popular line of work to improve efficiency from CNN weight sparsity, as reducing computation due to repeated zero weights is a special case of reducing computation due to repeated weights. To exploit weight repetition, this paper proposes a new CNN accelerator called the Unique Weight CNN Accelerator (UCNN). UCNN uses weight repetition to reuse CNN sub-computations (e.g., dot products) and to reduce CNN model size when stored in off-chip DRAM - both of which save energy. UCNN further improves performance by exploiting sparsity in weights. We evaluate UCNN with an accelerator-level cycle and energy model and with an RTL implementation of the UCNN processing element. On three contemporary CNNs, UCNN improves throughput-normalized energy consumption by 1:2x-4x, relative to a similarly provisioned baseline accelerator that uses Eyeriss-style sparsity optimizations. At the same time, the UCNN processing element adds only 17-24% area overhead relative to the same baseline. ## Authors Kartik Hegde (University of Illinois at Urbana-Champaign) Jiyong Yu (University of Illinois at Urbana-Champaign) Rohit Agrawal (University of Illinois at Urbana-Champaign) Mengjia Yan (University of Illinois at Urbana-Champaign) [Michael Pellauer](/index.php/person/michael-pellauer) Christopher W. Fletcher (University of Illinois at Urbana-Champaign) ## Publication Date Saturday, June 2, 2018 ## Published in [International Symposium on Computer Architecture (ISCA)](https://dl.acm.org/doi/10.1109/ISCA.2018.00062) ## Research Area [Artificial Intelligence and Machine Learning ](/index.php/research-area/machine-learning-artificial-intelligence) [Computer Architecture](/index.php/research-area/computer-architecture) ## External Links [ACM Digital Library](https://dl.acm.org/doi/10.1109/ISCA.2018.00062) ## Uploaded Files [Published manuscript](https://d1qx31qr3h6wln.cloudfront.net/publications/UCNN_ISCA18_FINAL.pdf "Open file in new window")2.01 MB ## Copyright Copyright by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or . The definitive version of this paper can be found at ACM's Digital Library .