6.
Deployment
navigate_next
6.1.
芯片
search
Quick search
code
Show Source
PDF
Github
Dive into cheap deep learning
Table Of Contents
Getting Started
1. Introduction
1.1. time
1.2. 技术
1.3. 隐私
1.4. money
1.5. Data
2. Lightweight
2.1. Lightweight
2.2. SqueezeNet
2.3. MobileNet
2.4. MobileNet-v2
2.5. ShuffleNet
2.6. GhostNet
3. Compression
3.1. 模型压缩
3.2. 参数剪枝(Pruning)
3.3. Knowledge-Distillation
3.4. 量化
4. Write code
4.1. Jupyter
4.2. API
5. Train
5.1. Server
5.2. Active Learning
5.3. Pretrain
5.4. 改进
5.5. 结构
6. Deployment
6.1. 芯片
6.2. Edge
6.3. mobile
6.4. MCU
6.5. AI 中台
Dive into cheap deep learning
Table Of Contents
Getting Started
1. Introduction
1.1. time
1.2. 技术
1.3. 隐私
1.4. money
1.5. Data
2. Lightweight
2.1. Lightweight
2.2. SqueezeNet
2.3. MobileNet
2.4. MobileNet-v2
2.5. ShuffleNet
2.6. GhostNet
3. Compression
3.1. 模型压缩
3.2. 参数剪枝(Pruning)
3.3. Knowledge-Distillation
3.4. 量化
4. Write code
4.1. Jupyter
4.2. API
5. Train
5.1. Server
5.2. Active Learning
5.3. Pretrain
5.4. 改进
5.5. 结构
6. Deployment
6.1. 芯片
6.2. Edge
6.3. mobile
6.4. MCU
6.5. AI 中台
6.1.
芯片
¶
移动硬件上跑深度学习，于是 MIT 的 Viviene Sze 发表了第一款深度学习加速芯片 Eyeriss.
Previous
6. Deployment
Next
6.2. Edge