---
# System prepended metadata

title: Seminar

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---
title: Seminar
---
# 1102 Seminar
[TOC]
## 02/25 AI & chip

> **EDA for AI** & **AI for EDA**
### NNeed: An **E**valuation and Design Space **E**xploration Engine for C**NN** Accelerators using Extensive **D**ataflow Analysis 
> [name=國立陽明交通大學電子工程學系 黃俊達教授]
- Architecture Developmemt & Algorithm Refinememt for Intelligent Computing
- EDA for AI
#### Introduction
- Biological NN -> Artificial Neural Network (ANN), MLPerception (MLP), Convolution NN, Recurrent NN
- Accelerator
    - SoC : high performance, low power
    - two PEs: 
        - **Fine-grained** architecture
            - high flex
            - simple
            - high regularity and capacity
        - **Coarse-grained** architecture
            - Few complex PEs
            - High utilization
            - Low power and small area
- Systolic-Array based
#### Motivation and Previous Work
- Accelerator Design
    - **HUGE** design space
    - Need an ultra-eff egine to speed up the whole eval process
- eval egine
    - practical hw
    - effi dataflow
    - rich ana
    - fast turnaround time
(CNN model, config., constraints->ana egine->perform power area, hw arch.)
- simulation vs. analysis
    - trade-off (speed / accuracy)
![](https://i.imgur.com/gtRc59p.jpg)
![](https://i.imgur.com/StuDcEW.jpg)
#### Methology
![](https://i.imgur.com/uiwDlhH.jpg)
- which one is constraint? weight or input?
- **given constraint, then it could give you a best solution**
#### Hardware Template
- PE array
- Global buffer
- DRAM
- Input Stationary
    ![](https://i.imgur.com/ZXuRWpj.jpg)
- Weight Stationary
- Output Stationary
- Tiles and Tasks
    - task is a tile-level computation job
- Task Sequence Example
    ![](https://i.imgur.com/nevl8XN.jpg)
- External Data Manhrmrny
- Traditional Dataflows
- Dataflow and Data Reuse
    - IS, WS, OW is only be consider from PEs, so in general, there are $3!$ possible computation (I/W/O)
![](https://i.imgur.com/nO5YU2y.jpg)
![](https://i.imgur.com/LuFYQEy.jpg)
![](https://i.imgur.com/pFp0Ygk.jpg)
![](https://i.imgur.com/3oARXZh.jpg)
- there are two kinds of tasks: data-bound, compute-bound
    - compute-bound: a better PE
    - data-bound: higher DRAM bandwidth

#### Experiment Result
![](https://i.imgur.com/8YgRUp5.jpg)
- depend on what you want
- In general, owi/oiw perform better then others, but it is **in generel**, different results in different layers (data will be smaller after long-term training, but weights are going to be extremely large)
![](https://i.imgur.com/dRHlkhY.jpg)
- performance curve is **NOT** symmetric, but at first we will give it a square shape (**NOT** always the best)

### When AI meet EDA (Electronic Design Automation) -- 以類比設計自動化為例
> [name=國立陽明交通大學電機系 劉建男教授兼系主任]
- 國內電子設計翹楚!! (面不改色說著幹話🍵)
- character: 一本正經說幹話, 應該是面癱
- VSLI/CAD (電腦輔助設計)
- AI for EDA
#### Why EDA is important in IC design
- IC 設計最上游產業 (積體電路設計最重要的軟體)
- 精密儀器跟EDA軟體的需求(中美貿易戰如何打壓hauwei)
- CAD Research in VLSI Design
- more productivity -> CAD (Computer-Aided Design)
    - exist everywhere
#### What CS can do in EDA
- synthesis = automation + optimization
    - optimize for performance
    - optimize for yield
#### Machine Learning for everything
- AI in EDA Tools: data analysis and management + ML + optimization
#### **ML-assisted evolu algorithm for circuit sizing**
![](https://i.imgur.com/OTuB0y8.jpg)
![](https://i.imgur.com/uca1CLQ.jpg)
![](https://i.imgur.com/dz6eAHP.jpg)
![](https://i.imgur.com/CKt0Kil.jpg)
![](https://i.imgur.com/TWuZuWG.jpg)
![](https://i.imgur.com/eVOIzAK.jpg)
![](https://i.imgur.com/V9ixmS1.jpg)
- ML is used for predicting the design (after evolutionary operation)
![](https://i.imgur.com/MQakCKe.jpg)
- DNN is better than SVM (too simple)
#### conclusion
- ML + para = speed up at least 20 times
![](https://i.imgur.com/8djK3OB.jpg)
![](https://i.imgur.com/f0XJTmI.jpg)
![](https://i.imgur.com/DkhscTu.jpg)
![](https://i.imgur.com/fYmoOnO.jpg)
- EA + DNN + Force Model(their design) -> yield 97.5%

---

## 03/04 ZYXEL 合勤科技產品事業、人資經理
### 產業
> 產品事業管理部 葉宗昀(Gavin Yeh) 資深經理
- What are they doing ?
    - ADSL、電信業、5G、資安
    - 防火牆、網路設計代工
    - 軍事武器訊號技術(ex. 雷達)
- R&D 人才培育
- 疫情下的市場趨勢
    - 網路已變必需品
    - IoT Connected devices 依賴度提高
        - 利用 Internet 提升商品附加價值
    - Opportunity
        - E-Commerce
        - Digital Payment
        - Digital Media Spend
        - Social Media
    - Threatens
        - Cyberattacks
        - Ransomware Extortion
        - Fake News/ Information
        - Fraud
        > 危機就是轉機
    - Security
- Telco 電信 
    - Fiber 成本下降、收益提升(網速越快收費越高)
        - Average Billing per user Increasing
        - Operating Expense Decreasing
    - Fixed-Mobile Convergence Strategy
        - 綑綁消費
:::success
找國內外上市公司的財報，了解公司分析的未來趨勢及發展方向
（順便看股票好不好😄，以便未來投資
:::
![](https://i.imgur.com/0ZZsnIB.png)
- 軟體開發需求
    - 知識財: 手機的作業系統是價值所在
    - 比起會某一種程式語言，注重基本邏輯能力、學習能力
    - 主要開發 **Middle Ware** base on Linux kernel
- Solution 調整
- 瑪斯克的Backhaul
### 人資
> 全球人資暨行政處 黃惠玲(Feeling Huang) 資深經理
- 
賣服務


---
## 03/11 Poly 數位學習教育方案(產品發表)
> 賴巍政經理
- Polycom & Plantronic
    - NASA 指定耳機
- 服務需求，問題解決
    - 遠距教學
        - 軟體跨平台
        - 鏡頭追蹤: 講者、移動、多人。
            - 有多種教學模式，因應故種教學方式
            - 2秒是人體舒適切換的速度
        - 收音: 高清、定位人聲、防ECHO、降低環境噪音
            - Speaker tracking 聲波(束波)定位
                - 利用多個麥克風所收到聲音的時間差來計算角度
            - 使用至少三個麥克風矩陣，做數波定位
- 噪音屏蔽AI、虛擬隔音牆。來屏蔽外界的聲音，只收人聲
    - 根據頻率來判斷人聲，屏蔽其他噪音

---
## 03/18 On the Overlapping Community Detection ProblemFile
> 彭勝龍
> 台北商業大學創意科技與產業設計系主任
### Introduction
- Social Networks
    - Node: Individual, Organization
    - Edge: Relation, Interaction
- Communities
    - A group of vertices having a high density of edges, and a lower density of edges between different groups.
- Clusters in PPI Networks
    - PPI: Protein-Protein Interaction
    - a cluster can form a gene team or be involved in a pathway
    > cluster: 群聚效果
- Community Detection
    - aims to discover a community structure
- Community Structures(**C**)
    - Disjoint
    - Overlapping(close to the truth)
- Quality Functions
    - Q fuction: dishoint
    - ![](https://i.imgur.com/9vbvuZ7.jpg)
    - EQ funcition: overlapping
    - ![](https://i.imgur.com/b2ERql8.jpg)
    > 兩 **C** 必較 Q value: 越高越好
    - 用來分析並比較分群演算法優劣
### A Neighbor Melding Algorithm
- Neighbor Fusion for two **C**
- node 具有高影響力者可加入該群 **C**
    > deg(u) >= max{E,E+1}
    > degree: node 相連數量
- 重複融合直至 Quality 沒有改善
- ![](https://i.imgur.com/ojowP7Y.jpg)

### Hierarchical Merging Algorithms
- It shows an evolution of communities in a social network.
- It can be treated as a trend of community integration.
- Binary Tree
    - Spilt(up to down) or Merge(down to up)
- A General Bottom-Up Approach
    - ![](https://i.imgur.com/b8Ezxky.jpg)

#### EAGLE Algorithm
- Maximal Cliques
    - Find out all basic communities
- Compute similarity
#### Our Algorithm
- Blocks of close neighborhoods
    - a maximal biconnected component of a graph
### Coucluting Result
- Are th quality function good enough?
    - A partition without any cut get Q = 0.083
- How to measure an evolution tree for predicting a trend of community integration?

----
## 03/25 產業AI化 - 台灣人工智慧學校
> [台灣人工智慧學校 官網](https://aiacademy.tw/)
> 蔡明順 校務長
- Outside-in 從需求的角度看資工專業的潛力
    - 智慧路燈(含wifi等功能)
        - ![](https://i.imgur.com/wVJ48pE.png)
- 人工智慧產業化
- 科技產業是現代最重要的公司
    - 產業轉型(石油 --> 金融 --> 軟硬體科技)
    - 軟體吃掉硬體，AI吃掉軟體
    - AI是主流，其他像是元宇宙之類的，只是AI的附屬
    - AI已被各國當成國家戰略
        - 先進製造業、量子資訊科學、5G/B5G、AI是US政府投資的四逛關鍵技術
    - ![](https://i.imgur.com/dwKllmd.jpg)
- 數據、場景、演算法、半導體晶片與？？是AI重要的關鍵
- 現代進入Software2.0
- ![](https://i.imgur.com/hAD4MnE.jpg)
- Internet拉平了「資訊量」， AI拉平了「智能量」的差距
- AI產業化（AI+） vs. 產業AI化（+AI）
### 產業應用
- 智慧製造瑕疵檢測
    - Edge AI
- AOI (Automated optical inspection)
- 智慧醫療 Prognostic and Health Management 
    - 避免誤診
    - 仍在初期
- 智慧交通 - 智慧駕駛
    - ![](https://i.imgur.com/L6Qmj4K.png)
- 智慧金融
- AI發展程度，現在大概發展到level2、3
    ![](https://i.imgur.com/8n2m594.jpg)
- 資訊安全未來需求很大
- [MLOps](https://en.wikipedia.org/wiki/MLOps)
### Question
- 您剛剛特別以德系和美系來區分外商公司，請問他們有甚麼不一樣的地方?
- Edge AI是拿來加速的嗎?
- Data Enginner vs Data Scientist
- 產業 AI 諮詢? 產業有需求 我們握有技術但不了解 請問現在有什麼平台協助二方面的協調嗎

---
## 04/01 YOLOv4 及其產業應用
> [name=中央研究院 廖弘源 特聘研究員/所長]
> 人工智慧學校副校長
> IEEE fellow

### 人工智慧在音樂上的應用
- Using AI automatically sequence the song
    - to complete music vedio
e.g. 演唱會會有 serverl fans record and official version，用 AI turn them into a complete vedio

#### Issues needed to be addressed
- 各個影片觀看順序 - visual storytelling
- The diffrent of smartphone visual/audio quality
- language of flim

#### Potential Issue
- How to present the emotion, ideas, and art of a music director into a concert video mashup process?
- 符合音樂導演要求和經驗

#### Solution
- Visual
    - Shot Classification based on Error-Weighted Deep Cross-Correlation Model
        - Define the six camera shots by distance
        - basically a pattern matching problem + correlation
        - EW-Deep-CCM 17% error remain, sepcific in the simple shot (cause too many shot changes)
        - Input: 1000+ category of objects, video stream from adele concert
        - Process: Fuse the two using a Deep Neural network
        - Ouput: 8 categories of stage shot
- Conditional Random ield-based (CRF)Apporach1.
    - + CRF -> 90% :+1: 
    
### Seethevoice: Learning from Music to visual storytelling of shots
- 用音樂驅動並直接選取適當的視訊片段
    (Use AI pick a shot by music audio)

### Problem
- Must achieve real time response, which shot type to adopt

### System Framework
- KD
    - Train a teacher net and a student net, teacher net
        - Knowledge Distillation
- He who teaches, learns
    - The student info turn back to teach teacher net, then teacher distill again till the model stable.


### Yolov4
- The world’s most **accurate and speedy** object detector during (Sept. 28 – Dec. 15, 2020)
- Smart City Traffic Flow Solutions
    - Edging computing but use limited device ( TX2 )
- final goal: reinforcement learning -> 控制交通號誌
- object detection is a must (and Yolov4 is a detection model)
    - traffic detection 貢獻之一: 轉向bounding box (YOLO V4 default is 正向)



### Questions
- Q1. What is the difference between the purpose for two prediction (Dense prediction, Sparse prediction)
- Q2. Occlusion problem
    - Multiple Object Tracking 

## 04/15 Clustered-based Multi-pin Fine Pitch Ball Grid Array Substrate Routing Optimization 
> 劉一宇 台科大資工系 副教授
- 晶片設計
    - 硬體描述語言
    - EDA
- FBGA (Fine-pitch Ball Grid Array)
- Substrate: 晶片載板
- Routing: 繞線，避免線之間接觸導致短路。
- via: 在不同層電路間的樓梯
### Introduction
- Fine-pitch: 距離約等於 8~10 根頭髮(外面那些銀白色球體的間距)
![](https://i.imgur.com/CQ3qiVr.png)
- Structure
![](https://i.imgur.com/soQST8z.png)
![](https://i.imgur.com/z6O1Vst.jpg)
- Flip-chip
- 繞線技術越精細、成本越高
    - Via 打孔受限於成本，size 為線體的 4~6 倍
- finger 的部分採用 any angle
- 手機製造
    - 晶片: 10W
        - 電壓: 1V
        - 電流: 10A
### ILP(Interger Linear Programming)
- 任何要連接的兩點(起點finger --> 終點球體)
    - keep-out zone(for finger)
    ```
    in --> (node) --> out
    1                  1
    ```
- Constraints
    - in + out <= 2
    - out - in
        - 1: 起點
        - -1: 終點
        - 0: 過路的或是雜魚
- Relaxation
### Multi-pin Net Issue
- 前面討論的都是 two-pin，兩個點之間連接
- Algorithm
    1. Design
    2. Grouping
    3. Spanning Tree(Delaunay Triangulation)
    4. Connection
        - Intra: finger to finger, bump ball to bump ball
        - Inter: finger to bump ball
        > 遠距離就交給 ILP 處理
    5. Layout

---
## 04/15 Metaverse 元宇宙
> 佐臻 company
> AR Glasses
> New Brain-Computing Interface

## 04/29 我在「幸福企業」- 10年來學到的事情
> 王鐘逸 研究員

p.32 學長在進入業界後，有沒有遺憾當初在學期間沒有去做到的事情?或是如今學長看著底下帶的新人，有覺得我們需要多去努力的部分?

- 英文能力
- 快速抓到重點
ps. 不過他也覺得溝通很重要

Q: 那請問學長會用甚麼方式來練習溝通? 除了平常報告之外。

## 05/20 量子霸權與物聯網時代之密碼策略 
> 許宏誌 助理教授
> 量子(電腦)簡介：與傳統bit不同，一個位元可以同時存在0 or 1，測量時，0 & 1會依據他的機率出現。 (我這樣講不知道你聽不聽的懂)
> 博士論文: High-capacity and Efficient-Embedding Steganography

### Threats
![](https://i.imgur.com/VkItTKE.png)
#### Quantum Supremacy
- **Shor's Algorithm**: 質因數分解演算法
    - Threats to public-key crypto-systems
    - **Input**: an odd composite number with n bits
    - **Output**: a non-trival factorization of **Input** with some probability.
    - Quantum parallelism(量子位元之間的糾纏)
    - Reduce to order-finding problem
    - 用在 **RSA**
    - Time complexity: O(n^3logn)

![](https://i.imgur.com/cDfACiG.png)
- **Grover's Algorithm**: 無序資料庫搜索演算法
    - Security-level shrinkage of private-key crypto-systems
    - Search database with probabilities.
    - O(N) -> O(sqrt(N))
    - The security level of AES128 will shrink from 2^256 to 2^128. 
    - ![](https://i.imgur.com/gT3SuLh.png)

#### Internet of Things (IOT)
- Side-Channel Attack for private-key crypto-systems
    - **SCA 旁道攻擊**
        - ![](https://i.imgur.com/du5SlED.png)
        - 幫忙縮短時間

### Solutions
- For quantum computing
    - public-key crpto-systems
        - Post quantum cryptography
    - private-key crpto-systems
        - Extending the key length of AES
- For side-channel attack in IoT
    - Customized AES
    - Signal masking
    - Pipeline

![](https://i.imgur.com/XElcGDh.png)
> Candidate: 需支援七種加解密方式

#### NP-hard
- ex. 離散對數問題
- **Non-deterministic Polynomial**
- [什麼是P, NP, NP-Complete, NP-Hard問題](https://www.ycc.idv.tw/algorithm-complexity-theory.html)
#### Lattice
- Shortest Vector Problem
    - Given basis of a vector spcace V and ...><
    - ![](https://i.imgur.com/yfNbzgD.png)

#### Recall RSA
![](https://i.imgur.com/ev965ho.png)

#### [NTRUEncryption](https://en.wikipedia.org/wiki/NTRUEncrypt)
![](https://i.imgur.com/Pws6aos.png)
![](https://i.imgur.com/nFNwN29.png)
![](https://i.imgur.com/NX71jSm.png)
![](https://i.imgur.com/87ZHzGX.png)
![](https://i.imgur.com/vTbQdAk.png)
![](https://i.imgur.com/3uaMO9h.png)
![](https://i.imgur.com/5fn6Ai9.png)
![](https://i.imgur.com/HNgxOlk.png)
![](https://i.imgur.com/IRzZIC1.png)

#### [McEliece](https://en.wikipedia.org/wiki/McEliece_cryptosystem)
![](https://i.imgur.com/6S1jddX.png)

### Conclusion
![](https://i.imgur.com/RBXl5dg.png)

### QA
- 國內商機(業界)
    - 軍方需求
**Post-quantum cryptography(PQC)**
- 台灣現在還在開發量子電腦