> For the complete documentation index, see [llms.txt](https://paper.lingyunyang.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://paper.lingyunyang.com/reading-notes/conference/asplos-2017/neurosurgeon.md).

# Neurosurgeon: Collaborative intelligence between the cloud and mobile edge

## Meta Info

Presented in [ASPLOS 2017](https://doi.org/10.1145/3037697.3037698).

Authors: Yiping Kang, Johann Hauswald, Cao Gao, Austin Rovinski, Trevor Mudge, Jason Mars, Lingjia Tang (UMich).

## Understanding the paper

### TL;DRs

This paper presents **Neurosurgeon**, a lightweight scheduler to *automatically partition DNN computation* between mobile devices and data centers *at the granularity of neural network layers*.

It doesn't require per-application profiling.

### Overview

<figure><img src="/files/vlrcZQN8U966I0Gb369f" alt=""><figcaption><p>System Overview.</p></figcaption></figure>

### Dynamic DNN Partitioning

1. Analysis of the target DNN (use the prediction models to estimate)
2. Partition point selection (for *best end-to-end latency* or *best mobile energy consumption*)

### Related Work

Previous research efforts focus on *offloading computation from the mobile to the cloud*.