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研究生:林彥廷
研究生(外文):Lin, Yen-Ting
論文名稱:應用於卷積神經網路模型的心脈陣列加速器設計與分析
論文名稱(外文):Design and Analysis of Systolic Array-Based Accelerators for Convolutional Neural Networks
指導教授:吳誠文
指導教授(外文):Wu, Cheng-Wen
口試委員:黃稚存黃錫瑜謝明得
口試委員(外文):Huang, Chih-TsunHuang, Shi-YuShieh, Ming-Der
口試日期:2020-03-06
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:86
中文關鍵詞:心脈陣列卷積神經網路人工智慧電阻式隨機存取記憶體量化計算機結構
外文關鍵詞:Systolic ArrayConvolutional Neural NetworksArtificial IntelligenceResistive Random-Access MemoryQuantizationComputer Architecture
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近年來由於人工智慧(AI)應用領域的蓬勃發展,低功耗的深度神經網路(DNN)加速器於終端裝置的需求與日俱增。這些加速器必須在可接受的運算精確度下有好的運作效率。我們提出了系統性的方法設計心脈陣列,並對其進行分析。基於先前提出的矩陣乘積之心脈陣列的映射演算法,此論文拓展其方法於二維卷積運算之心脈陣列架構的設計。我們以幾種具有不同架構特性之心脈陣列架構為例,它們具有不同的運算平行度、不同的層運算型態等。我們實作了DNN加速器的模擬器,它包含了不同的心脈陣列架構以及其它周邊電路以評估它們的吞吐量、運算延遲、硬體使用率、及DNN的運算精確度等。對於固定的DNN模型,模擬器中每層運算所使用的硬體架構可依其規格需求各自調整,即不同的層架構配置。對於不同資料處理平行度的層架構配置,增加配置平行度能使DNN模型的運作加速3.5倍,並有3.0倍的硬體使用率,而其僅會增加最多約37%的面積。這些心脈陣列依循權重值靜止數據流的特性所設計,因此其中的次級運算單元陣列得以使用新興非揮發型記憶體(NVM)來實作,例如電阻式隨機存取記憶體(RRAM)。比較次級運算單元陣列的不同實作方式,能發現使用RRAM陣列實作者較使用數位運算單元陣列者,其面積小2至3個數量級。然而,由於每次運算的輸入數據及權重數據必須對RRAM單元多次操作,以致其運算延遲會提升至多64倍。考慮權重數據和激勵數據的不同量化位元數,暫存器大小、運算區塊的複雜度、RRAM陣列的大小等皆能縮減許多,其面積差異至少47%。
In recent years, along with the development of applications on artificial intelligence (AI), low power Deep Neural Network (DNN) accelerator is demanded incrementally by the commercial end-point devices. The accelerators are required the acceptable accuracy and perform high efficiency. In this thesis, we propose a systemic methodology to design and analysis the systolic array. We develop systolic array of 2D-convolution based on the proposed mapping algorithm for matrix multiplication. We take more than one available systolic array architectures as the examples. They have different hardware characteristics for different projection parallelisms and operation types. We propose a DNN accelerator simulator with these systolic arrays and the peripheral circuits to evaluate their throughput, latency, utilization, and test accuracy. For a DNN model, the systolic array architectures in the simulator can be adjusted by the requirement of layers, i.e., different layer configuration. The layer configuration with larger parallelism may speedup 3.5 times and improve the utilization by 3.0 times with only up to 37% of additional area overhead. These systolic arrays are followed by the weight stationary dataflow. Its secondary PE arrays are suitable for implemented by the Resistive Random-Access Memory (RRAM). It can reduce the hardware cost about 2 or 3 orders compared to the digital PE array implementations. However, the computation latency may be increase up to 64x because of the operating latency overheads of RRAM. Considering the different quantization level, the size of register files, the complexity of computational block, and the size of RRAM array can be reduced to varying degrees. The area difference for many cases are at least 47%.
Abstract ----- i
List of Figures ----- iv
List of Tables ----- vii
Chapter 1 Introduction ----- 1
Sec. 1.1 Motivation ----- 1
Sec. 1.2 Systolic Array ----- 3
Sec. 1.3 Processing in Memory Using Emerging Non-Volatile Memory ----- 7
Sec. 1.4 Quantization ----- 9
Sec. 1.5 Proposed Approach ----- 11
Sec. 1.6 Thesis Organization ----- 12
Chapter 2 Fundamentals of Systolic-Array Design for Matrix Multiplication ----- 14
Sec. 2.1 Mapping Algorithm ----- 14
Sec. 2.2 Complexity Analysis of Systolic Array ----- 17
Sec. 2.3 Dataflow and Scheduling of Systolic Array ----- 18
Sec. 2.4 Systolic Array Examples ----- 19
Chapter 3 Designs for 2D-Convolutions in DNN ----- 24
Sec. 3.1 Mapping Algorithm for Simple 2D-Convolutions ----- 24
Sec. 3.2 Mapping Algorithm for 2D-Convolutions with 3D I/O ----- 29
Sec. 3.3 (3s, 3s) Projection ----- 30
Sec. 3.4 Parallelism 1: (2s, 2s, 2t) Projection ----- 32
Sec. 3.5 Parallelism m: (2s, 2+s, 2-t) Projection ----- 36
Sec. 3.6 Parallelism m2: (2+s, 2+s, 2-t) Projection ----- 38
Sec. 3.7 Specific Architecture: Different CNN Layer Types ----- 41
Sec. 3.7.1 Stride 2 ----- 41
Sec. 3.7.2 Depth-Wise Separable Operations ----- 43
Sec. 3.7.3 FC Layers ----- 44
Sec. 3.8 Summary ----- 45
Chapter 4 Simulation and Evaluation ----- 47
Sec. 4.1 DNN Accelerator Simulator ----- 47
Sec. 4.1.1 General Convolution Operations ----- 48
Sec. 4.1.2 Skip Paths ----- 49
Sec. 4.2 Different Implementations of Synapse Block ----- 49
Sec. 4.2.1 Digital PE Array ----- 50
Sec. 4.2.2 Analog RRAM Pseudo-Crossbar (2n-level cell) ----- 50
Sec. 4.2.3 Digital RRAM Pseudo-Crossbar (2-level cell) ----- 51
Sec. 4.3 Quantization Flow ----- 52
Sec. 4.4 Area Evaluations ----- 56
Sec. 4.4.1 Area of PE Arrays ----- 56
Sec. 4.4.2 Area of Delay Units and Output Data Registers ----- 58
Sec. 4.4.3 Area of The Other Blocks: Act, Avg, SkMul ----- 60
Sec. 4.4.4 Layer Configurations for DNN Hardware ----- 63
Sec. 4.5 DNN Model Implementations ----- 64
Sec. 4.6 Experimental Results ----- 67
Sec. 4.6.1 Utilization and Latency Analysis ----- 67
Sec. 4.6.2 Area Breakdown of Components ----- 69
Sec. 4.6.3 Area with Different Quantization Levels ----- 72
Sec. 4.6.4 Trade-Off: Area vs. Throughput and Utilization ----- 75
Sec. 4.6.5 Summary ----- 78
Chapter 5 Conclusion and Future Work ----- 80
Sec. 5.1 Conclusion ----- 80
Sec. 5.2 Future Work ----- 80
References ----- 82
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