Summary of Benchmarking Edge Ai Platforms For High-performance Ml Inference, by Rakshith Jayanth et al.
Benchmarking Edge AI Platforms for High-Performance ML Inference
by Rakshith Jayanth, Neelesh Gupta, Viktor Prasanna
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive study is conducted to compare the latency and throughput of various linear algebra and neural network inference tasks across CPU-only, CPU/GPU, and CPU/NPU integrated solutions. The findings show that the Neural Processing Unit (NPU) excels in matrix-vector multiplication and some neural network tasks, while GPU outperforms in matrix multiplication and LSTM networks. CPU is found to excel at less parallel operations like dot product. The study highlights the potential of heterogeneous computing solutions for edge AI, where diverse compute units can be strategically leveraged to boost accurate and real-time inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Edge computers are getting more popular because they help reduce communication delay and allow processing in real-time. To make sure that these devices work well with neural networks, a study compares different computer chips (CPU-only, CPU/GPU, and CPU/NPU). The results show that the NPU is really good at doing some math problems, while the GPU does better on other tasks. The CPU is best for smaller jobs that don’t need to be done quickly. This study shows how using different computer chips together can help make edge AI work faster and more accurately. |
Keywords
» Artificial intelligence » Dot product » Inference » Lstm » Neural network
