{"type":"ScholarlyArticle","title":"Algorithm-Hardware Co-Design for Efficient Brain-Inspired Hyperdimensional Learning on Edge (Extended Abstract)","authors":[{"name":"Yang Ni","slug":"yang-ni","given":"Yang","family":"Ni","affiliation":"University of California Irvine"},{"name":"Yeseong Kim","slug":"yeseong-kim","given":"Yeseong","family":"Kim","affiliation":"Daegu Gyeongbuk Institute of Science and Technology"},{"name":"Tajana Rosing","slug":"tajana-rosing","given":"Tajana","family":"Rosing","affiliation":"University of California San Diego"},{"name":"Mohsen Imani","slug":"mohsen-imani","given":"Mohsen","family":"Imani","affiliation":"University of California Irvine"}],"abstract":"In this paper, we propose an efficient framework to accelerate a lightweight brain-inspired learning solution, hyperdimensional computing (HDC), on existing edge systems. Through algorithm-hardware co-design, we optimize the HDC models to run them on the low-power host CPU and machine learning accelerators like Edge TPU. By treating the lightweight HDC learning model as a hyper-wide neural network, we exploit the capabilities of the accelerator and machine learning platform, while reducing training runtime costs by using bootstrap aggregating. Our experimental results conducted on mobile CPU and the Edge TPU demonstrate that our framework achieves 4.5 times faster training and 4.2 times faster inference than the baseline platform. Furthermore, compared to the embedded ARM CPU, Raspberry Pi, with similar power consumption, our framework achieves 19.4 times faster training and 8.9 times faster inference.","doi":"10.24963/ijcai.2023/723","url":"https://ijcai.org/proceedings/2023/723","pdf":"https://ijcai.org/proceedings/2023/0723.pdf","markdown":"https://ijcai.org/proceedings/2023/723.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/723","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":723,"pages":{"first":6474,"last":6479},"keywords":["Sister Conferences Best Papers: Machine Learning","Sister Conferences Best Papers: Knowledge Representation and Reasoning","Sister Conferences Best Papers: Multidisciplinary Topics and Applications"],"videos":[],"references":[{"position":1,"raw":"[Anguita and others, 2013] Davide Anguita et al. A pub- lic domain dataset for human activity recognition using smartphones. In ESANN, 2013.","title":"A public domain dataset for human activity recognition using smartphones","authors":"Davide Anguita","year":"2013","venue":"ESANN","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen and others, 2021] Ruimin Chen et al. Joint active search and neuromorphic computing for efficient data ex- ploitation and monitoring in additive manufacturing. Jour- nal of Manufacturing Processes, 71:743-752, 2021.","title":"Joint active search and neuromorphic computing for efficient data exploitation and monitoring in additive manufacturing","authors":"Ruimin Chen","year":"2021","venue":"Journal of Manufacturing Processes","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cole and Fanty, 1994] Ron Cole and Mark Fanty. ISO- LET. UCI Machine Learning Repository, 1994. DOI: https://doi.org/10.24432/C51G69.","title":"UCI Machine Learning Repository","authors":"Cole, Fanty, Ron Cole, Mark Fanty","year":"1994","venue":null,"doi":"10.24432/c51g69","url":"https://doi.org/10.24432/C51G69","internal_url":null},{"position":4,"raw":"[Dall'Ora et al., 2019] Nicola Dall'Ora, Stefano Centomo, and Franco Fummi. Industrial-iot data analysis exploiting electronic design automation techniques. In IWASI, pages 103-109. IEEE, 2019.","title":"Industrial-iot data analysis exploiting electronic design automation techniques","authors":"Nicola Dall, ' Ora, Stefano Centomo, Franco Fummi","year":"2019","venue":"IWASI","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Datta and others, 2019] Sohum Datta et al. A pro- grammable hyper-dimensional processor architecture for human-centric iot. JETCAS, 9(3):439-452, 2019.","title":"A programmable hyper-dimensional processor architecture for human-centric iot","authors":"Sohum Datta","year":"2019","venue":"JETCAS","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Genssler and others, 2021] Paul R Genssler et al. Brain- inspired computing for wafer map defect pattern classifi- cation. In ITC, pages 123-132. IEEE, 2021.","title":"Braininspired computing for wafer map defect pattern classification","authors":"Paul R Genssler","year":"2021","venue":"ITC","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Halawani et al., 2021] Yasmin Halawani, Dima Kilani, Eman Hassan, Huruy Tesfai, Hani Saleh, and Baker Mo- hammad. Rram-based cam combined with time-domain circuits for hyperdimensional computing. Scientific re- ports, 11(1):19848, 2021.","title":"Rram-based cam combined with time-domain circuits for hyperdimensional computing","authors":"Yasmin Halawani, Dima Kilani, Eman Hassan, Huruy Tesfai, Hani Saleh, Baker Mohammad","year":"2021","venue":"Scientific reports","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hernández-Cano and others, 2021a] Alejandro Hernández- Cano et al. Real-time and robust hyperdimensional classi- fication. In GLSVLSI, pages 397-402, 2021.","title":"Real-time and robust hyperdimensional classification","authors":"Hernández-Cano, Alejandro Hernández-Cano","year":"2021","venue":"GLSVLSI","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hernández-Cano and others, 2021b] Alejandro Hernández- Cano et al. Reghd: Robust and efficient regression in hyper-dimensional learning system. In DAC, pages 7-12. IEEE, 2021.","title":"Reghd: Robust and efficient regression in hyper-dimensional learning system","authors":"Hernández-Cano, Alejandro Hernández-Cano","year":"2021","venue":"DAC","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Imani and others, 2017a] Mohsen Imani et al. Exploring hyperdimensional associative memory. In HPCA, pages 445-456. IEEE, 2017.","title":"Exploring hyperdimensional associative memory","authors":"Mohsen Imani, Imani","year":"2017","venue":"HPCA","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Imani and others, 2017b] Mohsen Imani et al. Voicehd: Hy- perdimensional computing for efficient speech recogni- tion. In ICRC, pages 1-8. IEEE, 2017.","title":"Voicehd: Hyperdimensional computing for efficient speech recognition","authors":"Mohsen Imani, Imani","year":"2017","venue":"ICRC","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Imani and others, 2019] Mohsen Imani et al. A frame- work for collaborative learning in secure high-dimensional space. In CLOUD, pages 435-446. IEEE, 2019. [Imani and others, 2020] Mohsen Imani et al. Dual: Acceler- ation of clustering algorithms using digital-based process- ing in-memory. In MICRO, pages 356-371. IEEE, 2020.","title":"Dual: Acceleration of clustering algorithms using digital-based processing in-memory","authors":"Mohsen Imani","year":"2019","venue":"CLOUD","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kanerva, 2009] Pentti Kanerva. Hyperdimensional comput- ing: An introduction to computing in distributed represen- tation with high-dimensional random vectors. Cognitive computation, 1(2):139-159, 2009.","title":"Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors","authors":"Pentti Kanerva, Kanerva","year":"2009","venue":"Cognitive computation","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Karunaratne and others, 2020] Geethan Karunaratne et al. In-memory hyperdimensional computing. Nature Elec- tronics, 3(6):327-337, 2020.","title":"In-memory hyperdimensional computing","authors":"Geethan Karunaratne, Karunaratne","year":"2020","venue":"Nature Electronics","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Khaleghi and others, 2020] Behnam Khaleghi et al. Shear er: highly-efficient hyperdimensional computing by software-hardware enabled multifold approximation. In ISLPED, pages 241-246, 2020.","title":"Shear er: highly-efficient hyperdimensional computing by software-hardware enabled multifold approximation","authors":"Behnam Khaleghi","year":"2020","venue":"ISLPED","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Kim and others, 2017] Yeseong Kim et al. ORCHARD: vi- sual object recognition accelerator based on approximate in-memory processing. In ICCAD, pages 25-32. IEEE, 2017.","title":"ORCHARD: visual object recognition accelerator based on approximate in-memory processing","authors":"Others ; Yeseong Kim, Kim","year":"2017","venue":"ICCAD","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kim and others, 2020] Yeseong Kim et al. Geniehd: Ef- ficient DNA pattern matching accelerator using hyperdi- mensional computing. In DATE, pages 115-120. IEEE, 2020.","title":"Geniehd: Efficient DNA pattern matching accelerator using hyperdimensional computing","authors":"Others ; Yeseong Kim, Kim","year":"2020","venue":"DATE","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[LeCun and others, 1998] Yann LeCun et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.","title":"Gradient-based learning applied to document recognition","authors":"Yann Lecun","year":"1998","venue":"Proceedings of the IEEE","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Li et al., 2018] He Li, Kaoru Ota, and Mianxiong Dong. Learning iot in edge: Deep learning for the internet of things with edge computing. IEEE network, 32(1):96-101, 2018.","title":"Learning iot in edge: Deep learning for the internet of things with edge computing","authors":"Li","year":"2018","venue":"IEEE network","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Moin and others, 2021] Ali Moin et al. A wearable biosens- ing system with in-sensor adaptive machine learning for hand gesture recognition. Nature Electronics, 4(1), 2021.","title":"A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition","authors":"Ali Moin","year":"2021","venue":"Nature Electronics","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Pan and McElhannon, 2017] Jianli Pan and James McEl- hannon. Future edge cloud and edge computing for inter- net of things applications. IEEE IOT, 5(1):439-449, 2017.","title":"Future edge cloud and edge computing for internet of things applications","authors":"Mcelhannon Pan, Jianli Pan, James Mcelhannon","year":"2017","venue":"IEEE IOT","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Poduval and others, 2021a] Prathyush Poduval et al. Cogni- tive correlative encoding for genome sequence matching in hyperdimensional system. In DAC, pages 781-786. IEEE, 2021.","title":"Prathyush Poduval et al. Cognitive correlative encoding for genome sequence matching in hyperdimensional system","authors":"Others Poduval","year":"2021","venue":"DAC","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Poduval and others, 2021b] Prathyush Poduval et al. Stochd: Stochastic hyperdimensional system for efficient and robust learning from raw data. In DAC, pages 1195-1200. IEEE, 2021.","title":"Stochd: Stochastic hyperdimensional system for efficient and robust learning from raw data","authors":"Others ; Prathyush Poduval, Poduval","year":"2021","venue":"DAC","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Ram and others, 2021] Saswat Kumar Ram et al. Sehs: So- lar energy harvesting system for iot edge node devices. In Progress in Advanced Computing and Intelligent En- gineering, pages 432-443. Springer, 2021.","title":"Sehs: Solar energy harvesting system for iot edge node devices","authors":"Saswat Kumar, Ram","year":"2021","venue":"Progress in Advanced Computing and Intelligent Engineering","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Reiss and others, 2012] Attila Reiss et al. Introducing a new benchmarked dataset for activity monitoring. In ISWC, pages 108-109. IEEE, 2012.","title":"Introducing a new benchmarked dataset for activity monitoring","authors":"Others ; Attila Reiss, Reiss","year":"2012","venue":"ISWC","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Schmuck and others, 2019] Manuel Schmuck et al. Hard- ware optimizations of dense binary hyperdimensional computing: Rematerialization of hypervectors, binarized bundling, and combinational associative memory. JETC, 15(4):1-25, 2019.","title":"Hardware optimizations of dense binary hyperdimensional computing: Rematerialization of hypervectors, binarized bundling, and combinational associative memory","authors":"Manuel Schmuck","year":"2019","venue":"JETC","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Shafique and others, 2020] Muhammad Shafique et al. Ro- bust machine learning systems: Challenges, current trends, perspectives, and the road ahead. IEEE Design & Test, 2020.","title":"Muhammad Shafique et al. Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead","authors":"Others Shafique","year":"2020","venue":"IEEE Design & Test","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wu and others, 2018] Tony F Wu et al. Brain-inspired com- puting exploiting carbon nanotube fets and resistive ram: Hyperdimensional computing case study. In ISSCC, pages 492-494. IEEE, 2018.","title":"Brain-inspired computing exploiting carbon nanotube fets and resistive ram: Hyperdimensional computing case study","authors":"Tony F Wu","year":"2018","venue":"ISSCC","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zou and others, 2021a] Zhuowen Zou et al. Scalable edge- based hyperdimensional learning system with brain-like neural adaptation. In Proceedings of the International Conference for High Performance Computing, Network- ing, Storage and Analysis, pages 1-15, 2021.","title":"Scalable edgebased hyperdimensional learning system with brain-like neural adaptation","authors":"Others ; Zhuowen Zou, Zou","year":"2021","venue":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Zou and others, 2021b] Zhuowen Zou et al. Spiking hy- perdimensional network: Neuromorphic models inte- grated with memory-inspired framework. arXiv preprint arXiv:2110.00214, 2021.","title":"Spiking hyperdimensional network: Neuromorphic models integrated with memory-inspired framework","authors":"Others ; Zhuowen Zou, Zou","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}