PROPHET: Predictive On-Chip Power Meter in Hardware Accelerator for DNN
Jian Peng, Tingyuan Liang, Zhiyao Xie, Wei Zhang
Abstract
On-chip power meters play a critical role in power management by generating timely and accurate power traces at runtime. However, both performance-counter-based and existing RTL-based on-chip power meters have difficulty in providing sufficient response time for fast power and voltage management scenarios. Additionally, they can be costly to implement for large-scale DNN accelerators with many homogeneous process elements. To address these limitations, this paper proposes PROPHET, a data-pattern-based predictive on-chip power meter targeting multiply-accumulate-based DNN accelerators. By sampling pre-defined data patterns during memory access, PROPHET can predict power consumption before it actually happens. In our experiments, PROPHET predicts power consumption dozens of clock cycles in advance, with a temporal resolution of 4 clock cycles and NMAE < 7% and area overhead < 2% for various systolic-array-based DNN accelerators. PROPHET has the potential to enable fine-grained power management and optimization for large-scale DNN accelerators, improving their energy efficiency.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 56bfcaf1-4f76-4e4c-8309-de0c9ca6455fBuilds on1
Related papers
- Nona: Accurate Power Prediction Model Using Neural NetworksHoSun Choi, Chanho Park, Euijun Kim, William J. SongDAC 2024 · 1 citation
- ADA-GP: Accelerating DNN Training By Adaptive Gradient PredictionVahid Janfaza, Shantanu Mandal, Farabi Mahmud, Abdullah MuzahidMICRO 2023 · 3 citations
- Interstellar: Using Halide's Scheduling Language to Analyze DNN AcceleratorsXuan Yang, Mingyu Gao, Qiaoyi Liu, Jeff Setter et al.ASPLOS 2020 · 237 citations
- PowerGrad: Hierarchical Power Management for Power-Limited ML Inference ClustersHyoungwook Nam, Raghavendra Pradyumna Pothukuchi, Alper Buyuktosunoglu, Aporva Amarnath et al.ISCA 2026 · 1 citation
- Drift: Leveraging Distribution-based Dynamic Precision Quantization for Efficient Deep Neural Network AccelerationLian Liu, Zhaohui Xu, Yintao He, Ying Wang et al.DAC 2024 · 5 citations
