The DOI matches its Crossref record.
1.1 Computing's energy problem (and what we can do about it)
Abstract
Our challenge is clear: The drive for performance and the end of voltage scaling have made power, and not the number of transistors, the principal factor limiting further improvements in computing performance. Continuing to scale compute performance will require the creation and effective use of new specialized compute engines, and will require the participation of application experts to be successful. If we play our cards right, and develop the tools that allow our customers to become part of the design process, we will create a new wave of innovative and efficient computing devices.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2761740 · Cited by 4,215
Deep learning with coherent nanophotonic circuits
Shen, Y., Harris, N. C., Skirlo, S., Prabhu, M., Baehr-Jones, T., Hochberg, M., Sun, X., Zhao, S., Larochelle, H., Englund, D., et al. (2017) · Nature Photonics · doi:10.1038/nphoton.2017.93 · Cited by 3,340
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
Memory devices and applications for in-memory computing
Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R., & Eleftheriou, E. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0655-z · Cited by 2,289
In-memory computing with resistive switching devices
Ielmini, D., & Wong, H.-S. P. (2018) · Nature Electronics · doi:10.1038/s41928-018-0092-2 · Cited by 2,298
Fully hardware-implemented memristor convolutional neural network
Yao, P., Wu, H., Gao, B., Tang, J., Zhang, Q., Zhang, W., Yang, J. J., & Qian, H. (2020) · Nature · doi:10.1038/s41586-020-1942-4 · Cited by 2,179
Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016) · arXiv · doi:10.48550/arxiv.1602.02830 · Cited by 2,106
Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks
Chen, Y.-H., Emer, J., & Sze, V. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001177 · Cited by 1,182
A Survey of Quantization Methods for Efficient Neural Network Inference
Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M. W., & Keutzer, K. (2022) · Low-Power Computer Vision · doi:10.1201/9781003162810-13 · Cited by 1,219
“[96].”
InfluentialFederated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3090430 · Cited by 892
Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning
Yang, T.-J., Chen, Y.-H., & Sze, V. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.643 · Cited by 732
A crossbar array of magnetoresistive memory devices for in-memory computing
Jung, S., Lee, H., Myung, S., Kim, H., Yoon, S. K., Kwon, S.-W., Ju, Y., Kim, M., Yi, W., Han, S., et al. (2022) · Nature · doi:10.1038/s41586-021-04196-6 · Cited by 644
In-Memory Computing: Advances and Prospects
Verma, N., Jia, H., Valavi, H., Tang, Y., Ozatay, M., Chen, L.-Y., Zhang, B., & Deaville, P. (2019) · IEEE Solid-State Circuits Magazine · doi:10.1109/mssc.2019.2922889 · Cited by 473
Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review
Shuvo, M. M. H., Islam, S. K., Cheng, J., & Morshed, B. I. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3226481 · Cited by 436
Biswas, A., & Chandrakasan, A. P. (2019) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2018.2880918 · Cited by 414
ZeroQ: A Novel Zero Shot Quantization Framework
Cai, Y., Yao, Z., Dong, Z., Gholami, A., Mahoney, M. W., & Keutzer, K. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01318 · Cited by 381
A White Paper on Neural Network Quantization
Nagel, M., Fournarakis, M., Amjad, R. A., Bondarenko, Y., van Baalen, M., & Blankevoort, T. (2021) · arXiv · doi:10.48550/arxiv.2106.08295 · Cited by 323
Mengu, D., Luo, Y., Rivenson, Y., & Ozcan, A. (2020) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2019.2921376 · Cited by 255
Wang, J., Wang, X., Eckert, C., Subramaniyan, A., Das, R., Blaauw, D., & Sylvester, D. (2020) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2019.2939682 · Cited by 244
Mahroof, K. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.11.008 · Cited by 223
Movement pruning: Adaptive sparsity by fine-tuning
Sanh, V., Wolf, T., & Rush, A. M. (2020) · arXiv · doi:10.48550/arxiv.2005.07683 · Cited by 218
A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things
Du, L., Du, Y., Li, Y., Su, J., Kuan, Y.-C., Liu, C.-C., & Chang, M.-C. F. (2018) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2017.2735490 · Cited by 205
Hooker, S. (2021) · Communications of the ACM · doi:10.1145/3467017 · Cited by 211
Ham, S., Kang, M., Jang, S., Jang, J., Choi, S., Kim, T.-W., & Wang, G. (2020) · Science Advances · doi:10.1126/sciadv.aba1178 · Cited by 184
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01834 · Cited by 182
High-Performance FPGA-Based CNN Accelerator With Block-Floating-Point Arithmetic
Lian, X., Liu, Z., Song, Z., Dai, J., Zhou, W., & Ji, X. (2019) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2019.2913958 · Cited by 162
Efficient Federated Learning With Spike Neural Networks for Traffic Sign Recognition
Xie, K., Zhang, Z., Li, B., Kang, J., Niyato, D., Xie, S., & Wu, Y. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3178808 · Cited by 128
Lead federated neuromorphic learning for wireless edge artificial intelligence
Yang, H., Lam, K.-Y., Xiao, L., Xiong, Z., Hu, H., Niyato, D., & Vincent Poor, H. (2022) · Nature Communications · doi:10.1038/s41467-022-32020-w · Cited by 127
Sponge Examples: Energy-Latency Attacks on Neural Networks
Shumailov, I., Zhao, Y., Bates, D., Papernot, N., Mullins, R., & Anderson, R. (2021) · 2021 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp51992.2021.00024 · Cited by 125
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
Wiedemann, S., Kirchhoffer, H., Matlage, S., Haase, P., Marban, A., Marinc, T., Neumann, D., Nguyen, T., Schwarz, H., Wiegand, T., et al. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2969554 · Cited by 113
Neural inference at the frontier of energy, space, and time
Modha, D. S., Akopyan, F., Andreopoulos, A., Appuswamy, R., Arthur, J. V., Cassidy, A. S., Datta, P., DeBole, M. V., Esser, S. K., Otero, C. O., et al. (2023) · Science · doi:10.1126/science.adh1174 · Cited by 115
Accelerating CNN inference on FPGAs: A Survey
Abdelouahab, K., Pelcat, M., Serot, J., & Berry, F. (2018) · arXiv · doi:10.48550/arxiv.1806.01683 · Cited by 107
AI and ML Accelerator Survey and Trends
Reuther, A., Michaleas, P., Jones, M., Gadepally, V., Samsi, S., & Kepner, J. (2022) · 2022 IEEE High Performance Extreme Computing Conference (HPEC) · doi:10.1109/hpec55821.2022.9926331 · Cited by 109
How to Evaluate Deep Neural Network Processors: TOPS/W (Alone) Considered Harmful
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2020) · IEEE Solid-State Circuits Magazine · doi:10.1109/mssc.2020.3002140 · Cited by 80
Yoon, J. H., Song, Y.-W., Ham, W., Park, J.-M., & Kwon, J.-Y. (2023) · APL Materials · doi:10.1063/5.0149393 · Cited by 74
NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers
Liu, Y., Yang, H., Dong, Z., Keutzer, K., Du, L., & Zhang, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01946 · Cited by 60
Accelerating Retrieval-Augmented Generation
Quinn, D., Nouri, M., Patel, N., Salihu, J., Salemi, A., Lee, S., Zamani, H., & Alian, M. (2025) · Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1 · doi:10.1145/3669940.3707264 · Cited by 48
Noise-Resilient DNN: Tolerating Noise in PCM-Based AI Accelerators via Noise-Aware Training
Kariyappa, S., Tsai, H., Spoon, K., Ambrogio, S., Narayanan, P., Mackin, C., Chen, A., Qureshi, M., & Burr, G. W. (2021) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2021.3089987 · Cited by 45
AQD: Towards Accurate Quantized Object Detection
Chen, P., Liu, J., Zhuang, B., Tan, M., & Shen, C. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00017 · Cited by 29
Mobile-URSONet: an embeddable neural network for onboard spacecraft pose estimation
Posso, J., Bois, G., & Savaria, Y. (2022) · 2022 IEEE International Symposium on Circuits and Systems (ISCAS) · doi:10.1109/iscas48785.2022.9937721 · Cited by 27
“Thus, increasing image resolution is a more efficient solution to improve accuracy than removing the bottleneck as the power consumption highly depends on memory accesses [20].”
BackgroundSpinDrop: Dropout-Based Bayesian Binary Neural Networks With Spintronic Implementation
Ahmed, S. T., Danouchi, K., Münch, C., Prenat, G., Anghel, L., & Tahoori, M. B. (2023) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2023.3242146 · Cited by 25
Miyashita, D., Kousai, S., Suzuki, T., & Deguchi, J. (2016) · 2016 IEEE Asian Solid-State Circuits Conference (A-SSCC) · doi:10.1109/asscc.2016.7844126 · Cited by 25
CoNNa–Hardware accelerator for compressed convolutional neural networks
Struharik, R. J. R., Vukobratović, B. Z., Erdeljan, A. M., & Rakanović, D. M. (2020) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.102991 · Cited by 18
Edge ai: leveraging the full potential of deep learning
Shuvo, M. M. H. (2022) · Studies in Computational Intelligence · doi:10.1007/978-3-031-14748-7_2 · Cited by 18
FLASH: F ast Neura l A rchitecture S earch with H ardware Optimization
Li, G., Mandal, S. K., Ogras, U. Y., & Marculescu, R. (2021) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3476994 · Cited by 15
A Case for Emerging Memories in DNN Accelerators
Mukherjee, A., Saurav, K., Nair, P., Shekhar, S., & Lis, M. (2021) · 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) · doi:10.23919/date51398.2021.9474252 · Cited by 12
Post-Training Quantization for Energy Efficient Realization of Deep Neural Networks
Latotzke, C., Balim, B., & Gemmeke, T. (2022) · 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla55696.2022.00243 · Cited by 8
Rebound Effects in Methods of Artificial Intelligence
Willenbacher, M., Hornauer, T., & Wohlgemuth, V. (2022) · Progress in IS · doi:10.1007/978-3-030-88063-7_5 · Cited by 7
Gonugondla, S. K., Patil, A. D., & Shanbhag, N. R. (2020) · IEEE/ACM ICCAD · doi:10.1145/3400302.3415642 · Cited by 6
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02557-6 · Cited by 3
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016) · arXiv · doi:10.48550/arxiv.1609.07061
Metadata
- Authors
- Horowitz, M.
- Venue
- 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC)
- Publisher
- IEEE
- Published
- 2014-02
- Pages
- 10-14
- ISSN
- 0193-6530 (ISSN-L)
- OpenAlex
- W1999085092
- Cited by
- 2,069
- References
- 0
- FWCI
- 27.58
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.