# Benchmarking Machine Learning: How Fast Can Your Algorithms Go?

arXiv: https://arxiv.org/abs/2101.03219 · HTML: https://mlsanity.com/paper/2101.03219/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 50 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Built on by (most cited first, 3 of 3)

- [Efficient Semantic Segmentation on Edge Devices](https://mlsanity.com/paper/2212.13691/) (2022)
- [On-device Large Multi-modal Agent for Human Activity Recognition](https://mlsanity.com/paper/2512.19742/) (2025)
- [Beyond the Frame: Single and mutilple video summarization method with user-defined length](https://mlsanity.com/paper/2401.10254/) (2023)

## Abstract (arXiv)

This paper is focused on evaluating the effect of some different techniques in machine learning speed-up, including vector caches, parallel execution, and so on. The following content will include some review of the previous approaches and our own experimental results.