Chanakya: Learning Runtime Decisions for Adaptive Real-Time Perception
Authors: Anurag Ghosh, Vaibhav Balloli, Akshay Nambi, Aditya Singh, Tanuja Ganu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyse and evaluate Chanakya through the following experiments. We employ s AP metric [6] that coherently evaluates real-time perception, combining latency and accuracy into a single metric. |
| Researcher Affiliation | Collaboration | Anurag Ghosh1 Vaibhav Balloli2 Akshay Nambi3 Aditya Singh3 Tanuja Ganu3 1Carnegie Mellon University 2University of Michigan 3Microsoft Research India |
| Pseudocode | Yes | Algorithm 1: Obtaining Observations From Streaming Perception System |
| Open Source Code | Yes | Code can be viewed at https://github.com/microsoft/chanakya. |
| Open Datasets | Yes | All the results are reported on Argoverse-HD, unless stated otherwise. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | For a real-time perception system on a given hardware (P40 GPU),... Consider the task of migrating a streaming perception system from a device employing P40 GPU, to newer V100 GPU. |
| Software Dependencies | No | The paper mentions various models and frameworks but does not specify software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | We optimize configurations across two decision dimensions D = {Ds : {360, 480, 540, 640, 720}, Dnp : {100, 300, 500, 1000}}, i.e., detector scale and number of proposals. |