Approximate Computing Methods for Embedded Machine Learning

Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks

Chen, Y.-H., Krishna, T., Emer, J. S., & Sze, V. (2017) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2016.2616357 · Cited by 3,247 · see citing works

Deep Learning with Limited Numerical Precision

Gupta, S., Agrawal, A., Gopalakrishnan, K., & Narayanan, P. (2015) · arXiv · doi:10.48550/arxiv.1502.02551 · Cited by 1,364 · see citing works

Translational principles of deep brain stimulation

Kringelbach, M. L., Jenkinson, N., Owen, S. L. F., & Aziz, T. Z. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2196 · Cited by 853 · see citing works

Deep Learning with Limited Numerical Precision

Gupta, S., Agrawal, A., Gopalakrishnan, K., & Narayanan, P. (2015) · Cited by 707 · see citing works

Artificial neural networks in hardware: A survey of two decades of progress

Misra, J., & Saha, I. (2010) · Neurocomputing · doi:10.1016/j.neucom.2010.03.021 · Cited by 649 · see citing works

Bio-Inspired Imprecise Computational Blocks for Efficient VLSI Implementation of Soft-Computing Applications

Mahdiani, H. R., Ahmadi, A.-A., Fakhraie, S. M., & Lucas, C. L. (2010) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2009.2027626 · Cited by 621 · see citing works

AxNN

Venkataramani, S., Ranjan, A., Roy, K., & Raghunathan, A. (2014) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1145/2627369.2627613 · Cited by 259 · see citing works

Quality programmable vector processors for approximate computing

Venkataramani, S., Chippa, V. K., Chakradhar, S., Roy, K., & Raghunathan, A. (2013) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/2540708.2540710 · Cited by 257 · see citing works

ApproxANN: An Approximate Computing Framework for Artificial Neural Network

Zhang, Q., Wang, T., Tian, Y., Yuan, F., & Xu, Q. (2015) · Design, Automation, and Test in Europe · doi:10.7873/date.2015.0618 · Cited by 187 · see citing works

Scalable effort hardware design

Chippa, V. K., Mohapatra, D., Raghunathan, A., Roy, K., & Chakradhar, S. (2010) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/1837274.1837411 · Cited by 173 · see citing works

ApproxANN: an approximate computing framework for artificial neural network

Zhang, Q., Wang, T., Tian, Y., Yuan, F., & Xu, Q. (2015) · Design, Automation, and Test in Europe · doi:10.5555/2755753.2755913 · Cited by 137 · see citing works

Accelerating Deep Convolutional Networks using low-precision and sparsity

Venkatesh, G., Nurvitadhi, E., & Marr, D. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952679 · Cited by 123 · see citing works

Leveraging the error resilience of machine-learning applications for designing highly energy efficient accelerators

Du, Z., Lingamneni, A., Chen, Y., Palem, K. V., Temam, O., & Wu, C. (2014) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2014.6742890 · Cited by 115 · see citing works

Reduced-Precision Strategies for Bounded Memory in Deep Neural NetsPreprint

Judd, P., Albericio, J., Hetherington, T., Aamodt, T. M., Jerger, N. E., Urtasun, R., & Moshovos, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.05236 · Cited by 88 · see citing works

LookNN: Neural network with no multiplication

Samragh, M., Imani, M., Koushanfar, F., & Rosing, T. (2017) · Design, Automation, and Test in Europe · doi:10.23919/date.2017.7927280 · Cited by 86 · see citing works

Hierarchical Temporal Memory Features with Memristor Logic Circuits for Pattern Recognition

Krestinskaya, O., Ibrayev, T., & James, A. P. (2017) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2017.2748024 · Cited by 86 · see citing works

Approximate storage for energy efficient spintronic memories

Ranjan, A., Venkataramani, S., Fong, X., Roy, K., & Raghunathan, A. (2015) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/2744769.2744799 · Cited by 81 · see citing works

Exploiting approximate computing for deep learning acceleration

Chen, C., Choi, J., Gopalakrishnan, K., Srinivasan, V., & Venkataramani, S. (2018) · Design, Automation, and Test in Europe · doi:10.23919/date.2018.8342119 · Cited by 67 · see citing works

Designing Hardware for Machine Learning: The Important Role Played by Circuit Designers

Sze, V. (2017) · IEEE Solid-State Circuits Magazine · doi:10.1109/mssc.2017.2745798 · Cited by 58 · see citing works

Adaptive and Energy-Efficient Architectures for Machine Learning: Challenges, Opportunities, and Research Roadmap

Shafique, M., Hafiz, R., Javed, M. U., Abbas, S., Sekanina, L., Vašíček, Z., & Mrázek, V. (2017) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi.2017.124 · Cited by 55 · see citing works

Mixed-signal circuits for embedded machine-learning applications

Murmann, B., Bankman, D. J., Chai, E., Miyashita, D., & Yang, L. (2015) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2015.7421361 · Cited by 52 · see citing works

Approximation-aware Multi-Level Cells STT-RAM cache architecture

Sampaio, F., Shafique, M., Zatt, B., Bampi, S., & Henkel, J. (2015) · International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES) · doi:10.1109/cases.2015.7324548 · Cited by 52 · see citing works

Energy Efficient Neural Computing: A Study of Cross-Layer Approximations

Sarwar, S. S., Srinivasan, G., Han, B., Wijesinghe, P., Jaiswal, A., Panda, P., Raghunathan, A., & Roy, K. (2018) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2018.2835809 · Cited by 46 · see citing works

A power-aware digital feedforward neural network platform with backpropagation driven approximate synapses

Kung, J., Kim, D.-H., & Mukhopadhyay, S. (2015) · IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1109/islped.2015.7273495 · Cited by 45 · see citing works

STAxCache: An approximate, energy efficient STT-MRAM cache

Ranjan, A., Venkataramani, S., Pajouhi, Z., Venkatesan, R., Roy, K., & Raghunathan, A. (2017) · Design, Automation, and Test in Europe · doi:10.23919/date.2017.7927016 · Cited by 45 · see citing works

Gaussian process regression in vital-sign early warning systems

Clifton, L., Clifton, D. A., Pimentel, M. A. F., Watkinson, P., & Tarassenko, L. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6347400 · Cited by 37 · see citing works

Real-Time Embedded Machine Learning for Tensorial Tactile Data Processing

Ibrahim, A., & Valle, M. (2018) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2018.2852260 · Cited by 32 · see citing works

Approximate memory compression for energy-efficiency

Ranjan, A., Raha, A., Raghunathan, V., & Raghunathan, A. (2017) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1109/islped.2017.8009173 · Cited by 28 · see citing works

Approximate Multipliers Based on Inexact Adders for Energy Efficient Data Processing

Osta, M., Ibrahim, A., Chiblè, H., & Valle, M. (2017) · IEEE International Conference on New Generation of Circuits and Systems (NGCAS) · doi:10.1109/ngcas.2017.41 · Cited by 25 · see citing works

Inexact Arithmetic Circuits for Energy Efficient IoT Sensors Data Processing

Osta, M., Ibrahim, A., Chiblè, H., & Valle, M. (2018) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2018.8351839 · Cited by 16 · see citing works

Low Power Approximate Multipliers for Energy Efficient Data Processing

Osta, M., Ibrahim, A., Seminara, L., Chiblè, H., & Valle, M. (2018) · Journal of Low Power Electronics · doi:10.1166/jolpe.2018.1536 · Cited by 8 · see citing works

Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge

George, A., Ravindran, A., Mendieta, M., & Tabkhi, H. (2021) · IEEE Access · doi:10.1109/access.2021.3055775 · Cited by 71 · see citing works

Adaptive approximate computing in edge AI and IoT applications: A review

Damsgaard, H. J., Grenier, A., Katare, D., Taufique, Z., Shakibhamedan, S., Troccoli, T., Chatzitsompanis, G., Kanduri, A., Ometov, A., Ding, A. Y., et al. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103114 · Cited by 61 · see citing works

Energy-Latency Computation Offloading and Approximate Computing in Mobile-Edge Computing Networks

Younis, A., Maheshwari, S., & Pompili, D. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3360850 · Cited by 48 · see citing works

A Survey on Hardware Accelerator Design of Deep Learning for Edge Devices

Samanta, A., Hatai, I., & Mal, A. K. (2024) · Wireless Personal Communications · doi:10.1007/s11277-024-11443-2 · Cited by 28 · see citing works

Accelerated Image Processing Through IMPLY-Based NoCarry Approximated Adders

Seiler, F., & TaheriNejad, N. (2024) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2024.3426926 · Cited by 27 · see citing works

Algorithmic-Level Approximate Tensorial SVM Using High-Level Synthesis on FPGA

Younes, H., Ibrahim, A., Rizk, M., & Valle, M. (2021) · Electronics · doi:10.3390/electronics10020205 · Cited by 24 · see citing works

An Efficient Selection-Based kNN Architecture for Smart Embedded Hardware Accelerators

Younes, H., Ibrahim, A., Rizk, M., & Valle, M. (2021) · IEEE Open Journal of Circuits and Systems · doi:10.1109/ojcas.2021.3108835 · Cited by 23 · see citing works

Energy Efficient Implementation of Machine Learning Algorithms on Hardware Platforms

Osta, M., Alameh, M., Younes, H., Ibrahim, A., & Valle, M. (2019) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs46596.2019.8965157 · Cited by 19 · see citing works

Efficient Image Processing via Memristive-Based Approximate In-Memory Computing

Seiler, F., & TaheriNejad, N. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3438113 · Cited by 20 · see citing works

Systematic Literature Review on Cost-Efficient Deep Learning

Klemetti, A., Raatikainen, M., Myllyaho, L., Mikkonen, T., & Nurminen, J. K. (2023) · IEEE Access · doi:10.1109/access.2023.3275431 · Cited by 16 · see citing works

Scalable Approximate Computing Techniques for Latency and Bandwidth Constrained IoT Edge

George, A., & Ravindran, A. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-76063-2_20 · Cited by 14 · see citing works

An Energy Efficient System for Touch Modality Classification in Electronic Skin Applications

Osta, M., Ibrahim, A., Magno, M., Eggimann, M., Antonio Pullini, D., Gastaldo, P., & Valle, M. (2019) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2019.8702113 · Cited by 14 · see citing works

Random-based networks with dropout for embedded systems

Ragusa, E., Gianoglio, C., Zunino, R., & Gastaldo, P. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05414-4 · Cited by 13 · see citing works

Data Oriented Approximate K-Nearest Neighbor Classifier for Touch Modality Recognition

Younes, H., Ibrahim, A., Rizk, M., & Valle, M. (2019) · Conference on Ph.D. Research in Microelectronics and Electronics · doi:10.1109/prime.2019.8787753 · Cited by 13 · see citing works

ARXON: A Framework for Approximate Communication Over Photonic Networks-on-Chip

Sunny, F., Mirza, A., Thakkar, I. G., Nikdast, M., & Pasricha, S. (2021) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2021.3066990 · Cited by 12 · see citing works

An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation

Roy, A., Dutta, H., Griffith, H., & Biswas, S. (2022) · Sensors · doi:10.3390/s22072514 · Cited by 9 · see citing works

Latency Control for Distributed Machine Vision at the Edge Through Approximate Computing

George, A., & Ravindran, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23374-7_2 · Cited by 8 · see citing works

Energy Efficient Approximate 3D Image Reconstruction

Wu, Y., Asmann, A., Stewart, B. D., & Wallace, A. M. (2021) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2021.3116471 · Cited by 7 · see citing works

Approximate Feature Extraction for Low Power Epileptic Seizure Prediction in Wearable Devices

Taufique, Z., Kanduri, A., Altaf, M. A. B., & Liljeberg, P. (2021) · IEEE Nordic Circuits and Systems Conference (NorCAS) · doi:10.1109/norcas53631.2021.9599870 · Cited by 7 · see citing works

An automotive case study on the limits of approximation for object detection

Caro, M., Tabani, H., Abella, J., Moll, F., Morancho, E., Canal, R., Altet, J., Calomarde, A., Cazorla, F. J., Rubio, A., et al. (2023) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.102872 · Cited by 7 · see citing works

Accelerated 3D Image Reconstruction for Resource Constrained Systems

Asmann, A., Wu, Y., Stewart, B. D., & Wallace, A. M. (2020) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco47968.2020.9287749 · Cited by 6 · see citing works

FPGA Implementation of Approximate CORDIC Circuits for Energy Efficient Applications

Osta, M., Ibrahim, A., & Valle, M. (2019) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs46596.2019.8964758 · Cited by 5 · see citing works

DARK-Adders: Digital Hardware Trojan Attack on Block-based Approximate Adders

Mishra, V., Hassan, N., Mehta, A., & Chatterjee, U. (2023) · International Conference on VLSI Design · doi:10.1109/vlsid57277.2023.00080 · Cited by 5 · see citing works

Near Sensors Computation based on Embedded Machine Learning for Electronic Skin

Ibrahim, A., Younes, H., Alameh, M., & Valle, M. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.11.049 · Cited by 4 · see citing works

ARXON: A Framework for Approximate Communication over Photonic Networks-on-ChipPreprint

Sunny, F., Mirza, A., Thakkar, I. G., Nikdast, M., & Pasricha, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.08828 · Cited by 3 · see citing works

An approximate randomization-based neural network with dedicated digital architecture for energy-constrained devices

Ragusa, E., Gianoglio, C., Zunino, R., & Gastaldo, P. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08034-2 · Cited by 2 · see citing works

Efficient Reconfigurable Mixed Precision $$\ell _1$$ Solver for Compressive Depth Reconstruction

Wu, Y., Wallace, A. M., Mota, J. F. C., Asmann, A., & Stewart, B. D. (2022) · Journal of Signal Processing Systems · doi:10.1007/s11265-022-01766-3 · Cited by 1 · see citing works

Mez: A Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT EdgePreprint

George, A., Ravindran, A., Mendieta, M., & Tabkhi, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.13549

Approximate Computing Circuits for Embedded Tactile Data Processing

Osta, M., Ibrahim, A., & Valle, M. (2022) · Electronics · doi:10.3390/electronics11020190

Efficient Co-Approximate Parallel Compressive Depth Reconstruction on FPGA

Wu, Y., & McAllister, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888441

Hardware-Software Co-Design with MSDM Approximate Multipliers: Resource, Power and Quality Tradeoffs for CNNs and Generative Models

Thakur, K., & Chandel, R. (2026) · International Conference on Intelligent and Sustainable Electronics & Computing Technologies (INSECT) · doi:10.1109/insect68872.2026.11663836

Approximate Computing for Machine Learning Workloads: A Circuits and Systems Perspective

Sanyal, S., Negi, S., Raghunathan, A., & Roy, K. (2012) · Springer eBooks · doi:10.1007/978-3-030-98347-5_15

Algorithmic Level Approximate Computing for Machine Learning Classifiers

Younes, H., Ibrahim, A., Rizk, M., & Valle, M. (2019) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs46596.2019.8964974 · Cited by 12 · see citing works

Energy-efficient embedded machine learning algorithms for smart sensing systems

Osta, M. (2020) · CINECA IRIS Institutial Research Information System (University of Genoa) · doi:10.15167/osta-mario_phd2020-02-27

Guest Editors’ Introduction

Hellebrand, S., Henkel, J., Raghunathan, A., & Wunderlich, H.-J. (2018) · IEEE Embedded Systems Letters · doi:10.1109/les.2018.2789942 · Cited by 1 · see citing works

Approximate computing techniques in machine learning architectures

Kandpal, J., & Sharma, D. (2026) · CRC Press eBooks · doi:10.1201/9781003687924-3