Snapea: Predictive early activation for reducing computation in deep convolutional neural networks V Akhlaghi, A Yazdanbakhsh, K Samadi, RK Gupta, H Esmaeilzadeh 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture …, 2018 | 196 | 2018 |
Energy-efficient neural networks using approximate computation reuse X Jiao, V Akhlaghi, Y Jiang, RK Gupta 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE …, 2018 | 53 | 2018 |
Quiver: Using control perturbations to increase the observability of sensor data in smart buildings J Koh, B Balaji, V Akhlaghi, Y Agarwal, R Gupta arXiv preprint arXiv:1601.07260, 2016 | 25 | 2016 |
Resistive bloom filters: from approximate membership to approximate computing with bounded errors V Akhlaghi, A Rahimi, RK Gupta 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE …, 2016 | 13 | 2016 |
Lemax: learning-based energy consumption minimization in approximate computing with quality guarantee V Akhlaghi, S Gao, RK Gupta Proceedings of the 55th Annual Design Automation Conference, 1-6, 2018 | 11 | 2018 |
An efficient network on-chip architecture based on isolating local and non-local communications V Akhlaghi, M Kamal, A Afzali-Kusha, M Pedram Computers & Electrical Engineering 45, 430-444, 2015 | 10 | 2015 |
Accelerating local binary pattern networks with software-programmable FPGAs JH Lin, A Lotfi, V Akhlaghi, Z Tu, RK Gupta 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE …, 2019 | 6 | 2019 |
Associative convolutional layers H Omidvar, V Akhlaghi, H Su, M Franceschetti, R Gupta International Conference on Artificial Intelligence and Statistics, 3115-3123, 2021 | 2 | 2021 |
Algorithm-Hardware Optimization of Deep Neural Networks for Edge Applications V Akhlaghi University of California, San Diego, 2020 | 1 | 2020 |