應用於深度神經網絡加速器中靜態隨機存取記憶體之內建自我修復技術

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姓名

鄧凱云(Kai-Yun Deng) 查詢紙本館藏

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電機工程學系

論文名稱

應用於深度神經網絡加速器中靜態隨機存取記憶體之內建自我修復技術
(Built-In Self-Repair Scheme for SRAMs in Deep Neural Network Accelerators)

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至系統瀏覽論文 (2025-8-20以後開放)

摘要(中)

深度神經網絡(Deep Neural Networks, DNN)已經被廣泛地使用於人工智能應用。而典型的深度神經網絡系統加速器通常具有靜態隨機訪問記憶體(SRAM)，用來暫時儲存數據及資料。在此論文當中，我們提出了一種有效的內建自我修復方案(Built-in self-repair)，用來提高深度神經網絡系統加速器中靜態隨機訪問記憶體(SRAM)的良率。論文的第一部分，我們提出一種交換機制的技術，在有限制的降低推論精確度(inference accuracy)之下提高記憶體良率。而此交換機制技術可以與現有的內建冗餘分析(Built-in redundancy analysis)演算法相互結合。我們實現兩種與交換機制結合之內建冗餘分析方案，包含局部修復最大化(local-repair-most, LRM)以及全面的內建冗餘分析(exhaustive BIRA)兩種。實驗結果表明，經修改的局部修復最大化演算法與全面的內建冗餘分析演算法各別在記憶體大小256 千位元組且帕松分布均值為0.2~1.0 (1.0~3.0)的條件下進行模擬，可以提高修復率大約3.4% (30.7%)與3.5% (27.3%)，並且犧牲至多0.10% (0.73%)
及0.12% (0.95%) 於MobileNet 以及Res-Net-50 模型之推論精確度。論文的第二部分，我們為上述所提出的內建冗餘分析方案提供一個自動評估與驗證平台。在平台當中，內建冗餘分析編譯器可以產生我們所提出的內建冗餘分析的暫存器傳輸級(RTL)之設計。其中評估工具根據指定的深度神經網絡模型以及加速器的靜態隨機訪問記憶體，提供修復率以及推論精確度的預測。另外驗證平台的部分，可以產生Verilog 測試平台(testbench)用以驗證內建冗餘分析的暫存器傳輸級設計。

摘要(英)

Deep neural networks (DNNs) have been widely used for artificial intelligence applications. An accelerator in a DNN system typically has static random access memories (SRAMs) for data buffering. In this thesis, we propose an efficient built-in self-repair scheme for enhancing the yield of SRAMs in the accelerator of DNN systems. In the first part of this thesis, a swapping mechanism is proposed to increase the yield under the constraint of
inference accuracy reduction. The swapping mechanism can be integrated into existing built-in redundancy analysis (BIRA) algorithms. A local-repair-most (LRM) and an exhaustive BIRA algorithms are modified to include the swapping mechanism. Simulation results show that the modified LRM scheme and exhaustive BIRA schemes can gain about 3.4% (30.7%) and 3.5% (27.3%) increment of repair rate by sacrificing most 0.10% (0.73%) and 0.12% (0.95%) inference accuracy reduction for a MobileNet and ResNet-50 under the condition of injection fault with Poisson distribution mean value 0.2~1.0 (and in mean value 1.0~3.0) of 256 Kbyte memory size with 2D redundancy configuration, respectively. In the second part of this thesis, we present an automation, evaluation and verification platform for the proposed BIRA schemes. In the platform, a BIRA compiler is designed to generate the RTL of the proposed BIRA schemes. An evaluation tool is proposed to estimate the repair rate and inference accuracy for the SRAMs in a given accelerator executing a given DNN model. Finally, the platform can generate Verilog testbench for the verification of RTL design of
BIRAs.

關鍵字(中)

★ 內建自我修復技術
★ 修復率
★ 內建備份元件分析技術

關鍵字(英)

★ built in self repair
★ repair rate
★ built-in redundancy-analysis

論文目次

1 Introduction 1
1.1 Deep Neural Network System . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Deep Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.2 Neural Network Acceleration System . . . . . . . . . . . . . . . . . . 4
1.2 Memory Built-In Self-Repair Techniques . . . . . . . . . . . . . . . . . . . . 4
1.2.1 BISR Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.2 Built-In Redundancy Analysis . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Error Tolerance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Thesis Motivation and Contribution . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Proposed BISR Scheme for Memories in DNN Systems 9
2.1 Concept of Swap Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.1 Swap Mechanism Variability . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.2 RA Schemes with Swap Mechanism . . . . . . . . . . . . . . . . . . . 12
2.2 Proposed Heuristic BIRA Algorithm with Swap Mechanism . . . . . . . . . . 12
2.2.1 Local Bitmap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 Built-In Redundancy Analysis Algorithm . . . . . . . . . . . . . . . . 14
2.2.3 Design of the BIRA Circuit . . . . . . . . . . . . . . . . . . . . . . . 16
2.3 Proposed Exhaustive BIRA Algorithm with Swap Mechanism . . . . . . . . 25
2.3.1 CRESTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.3.2 Built-In Redundancy Analysis Algorithm . . . . . . . . . . . . . . . . 26
2.3.3 Design of the BIRA Circuit . . . . . . . . . . . . . . . . . . . . . . . 28
2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3 Evaluation and Verification Platform 34
3.1 Overall Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2 Evaluation Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2.1 Fault Map Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2.2 Redundancy Analysis Categories in DNN Accelerator . . . . . . . . . 37
3.2.3 Evaluation Outcome . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.3 Verification Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3.1 Verify Simulation and Design Result . . . . . . . . . . . . . . . . . . 40
3.3.2 Automatic RTL Generation of Proposed BIRA . . . . . . . . . . . . . 42
4 Experimental Result and Analysis 45
4.1 Repair Rate Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.1.1 Repair Rate of HRA-SW Algorithm . . . . . . . . . . . . . . . . . . . 45
4.1.2 Repair Rate of ERA-SW Algorithm . . . . . . . . . . . . . . . . . . . 49
4.1.3 Repair Rate of Comparison Results . . . . . . . . . . . . . . . . . . . 53
4.2 Inference Accuracy Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.2.1 Number of Swap Words and Inference Accuracy . . . . . . . . . . . . 58
4.2.2 Different Size of Memory and Inference Accuracy . . . . . . . . . . . 60
4.2.3 Two RA Schemes and Inference Accuracy . . . . . . . . . . . . . . . 62
4.3 Area Overhead . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3.1 Area Overhead of HRA-SW scheme . . . . . . . . . . . . . . . . . . . 64
4.3.2 Area Overhead of ERA-SW scheme . . . . . . . . . . . . . . . . . . . 65
5 Conclusion and Future Work 68

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指導教授

李進福(Jin-Fu Li)

審核日期

2020-8-24

推文