Re-designing Main Memory Subsystems with Emerging Monolithic 3D (M3D) Integration and Phase Change Memory Technologies

Over the past two decades, Dynamic Random-Access Memory (DRAM) has emerged as the dominant technology for implementing the main memory subsystems of all types of computing systems. However, inferring from several recent trends, computer architects in both the industry and academia have widely accepted that the density (memory capacity per chip area) and latency of DRAM based main memory subsystems cannot sufficiently scale in the future to meet the requirements of future data-centric workloads related to Artificial Intelligence (AI), Big Data, and Internet-of-Things (IoT). In fact, the achievable density and access latency in main memory subsystems presents a very fundamental trade-off. Pushing for a higher density inevitably increases access latency, and pushing for a reduced access latency often leads to a decreased density. This trade-off is so fundamental in DRAM based main memory subsystems that merely looking to re-architect DRAM subsystems cannot improve this trade-off, unless disruptive technological advancements are realized for implementing main memory subsystems. In this thesis, we focus on two key contributions to overcome the density (represented as the total chip area for the given capacity) and access latency related challenges in main memory subsystems. First, we show that the fundamental area-latency trade-offs in DRAM can be significantly improved by redesigning the DRAM cell-array structure using the emerging monolithic 3D (M3D) integration technology. A DRAM bank structure can be split across two or more M3D-integrated tiers on the same DRAM chip, to consequently be able to significantly reduce the total on-chip area occupancy of the DRAM bank and its access peripherals. This approach is fundamentally different from the well known approach of through-silicon vias (TSVs)-based 3D stacking of DRAM tiers. This is because the M3D integration based approach does not require a separate DRAM chip per tier, whereas the 3D-stacking based approach does. Our evaluation results for PARSEC benchmarks show that our designed M3D DRAM cellarray organizations can yield up to 9.56% less latency and up to 21.21% less energy-delay product (EDP), with up to 14% less DRAM die area, compared to the conventional 2D DDR4 DRAM. Second, we demonstrate a pathway for eliminating the write disturbance errors in single-level-cell PCM, thereby positioning the PCM technology, which has inherently more relaxed density and latency trade-off compared to DRAM, as a more viable option for replacing the DRAM technology. We introduce low-temperature partial-RESET operations for writing ‘0’s in PCM cells. Compared to traditional operations that write \u270\u27s in PCM cells, partial-RESET operations do not cause disturbance errors in neighboring cells during PCM writes. The overarching theme that connects the two individual contributions into this single thesis is the density versus latency argument. The existing PCM technology has 3 to 4× higher write latency compared to DRAM; nevertheless, the existing PCM technology can store 2 to 4 bits in a single cell compared to one bit per cell storage capacity of DRAM. Therefore, unlike DRAM, it becomes possible to increase the density of PCM without consequently increasing PCM latency. In other words, PCM exhibits inherently improved (more relaxed) density and latency trade-off. Thus, both of our contributions in this thesis, the first contribution of re-designing DRAM with M3D integration technology and the second contribution of making the PCM technology a more viable replacement of DRAM by eliminating the write disturbance errors in PCM, connect to the common overarching goal of improving the density and latency trade-off in main memory subsystems. In addition, we also discuss in this thesis possible future research directions that are aimed at extending the impacts of our proposed ideas so that they can transform the performance of main memory subsystems of the future

A Phase Change Memory and DRAM Based Framework For Energy-Efficient and High-Speed In-Memory Stochastic Computing

Convolutional Neural Networks (CNNs) have proven to be highly effective in various fields related to Artificial Intelligence (AI) and Machine Learning (ML). However, the significant computational and memory requirements of CNNs make their processing highly compute and memory-intensive. In particular, the multiply-accumulate (MAC) operation, which is a fundamental building block of CNNs, requires enormous arithmetic operations. As the input dataset size increases, the traditional processor-centric von-Neumann computing architecture becomes ill-suited for CNN-based applications. This results in exponentially higher latency and energy costs, making the processing of CNNs highly challenging. To overcome these challenges, researchers have explored the Processing-In Memory (PIM) technique, which involves placing the processing unit inside or near the memory unit. This approach reduces data migration length and utilizes the internal memory bandwidth at the memory chip level. However, developing a reliable PIM-based system with minimal hardware modifications and design complexity remains a significant challenge. The proposed solution in the report suggests utilizing different memory technologies, such as Dynamic RAM (DRAM) and phase change memory (PCM), with Stochastic arithmetic and minimal add-on logic. Stochastic computing is a technique that uses random numbers to perform arithmetic operations instead of traditional binary representation. This technique reduces hardware requirements for CNN\u27s arithmetic operations, making it possible to implement them with minimal add-on logic. The report details the workflow for performing arithmetical operations used by CNNs, including MAC, activation, and floating-point functions. The proposed solution includes designs for scalable Stochastic Number Generator (SNG), DRAM CNN accelerator, non-volatile memory (NVM) class PCRAM-based CNN accelerator, and DRAM-based stochastic to binary conversion (StoB) for in-situ deep learning. These designs utilize stochastic computing to reduce the hardware requirements for CNN\u27s arithmetic operations and enable energy and time-efficient processing of CNNs. The report also identifies future research directions for the proposed designs, including in-situ PCRAM-based SNG, ODIN (A Bit-Parallel Stochastic Arithmetic Based Accelerator for In-Situ Neural Network Processing in Phase Change RAM), ATRIA (Bit-Parallel Stochastic Arithmetic Based Accelerator for In-DRAM CNN Processing), and AGNI (In-Situ, Iso-Latency Stochastic-to-Binary Number Conversion for In-DRAM Deep Learning), and presents initial findings for these ideas. In summary, the proposed solution in the report offers a comprehensive approach to address the challenges of processing CNNs, and the proposed designs have the potential to improve the energy and time efficiency of CNNs significantly. Using Stochastic Computing and different memory technologies enables the development of reliable PIM-based systems with minimal hardware modifications and design complexity, providing a promising path for the future of CNN-based applications

Letter from the Special Issue Editor

Editorial work for DEBULL on a special issue on data management on Storage Class Memory (SCM) technologies

상변화 메모리 시스템의 간섭 오류 완화 및 RMW 성능 향상 기법

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2021.8. 이혁재.Phase-change memory (PCM) announces the beginning of the new era of memory systems, owing to attractive characteristics. Many memory product manufacturers (e.g., Intel, SK Hynix, and Samsung) are developing related products. PCM can be applied to various circumstances; it is not simply limited to an extra-scale database. For example, PCM has a low standby power due to its non-volatility; hence, computation-intensive applications or mobile applications (i.e., long memory idle time) are suitable to run on PCM-based computing systems. Despite these fascinating features of PCM, PCM is still far from the general commercial market due to low reliability and long latency problems. In particular, low reliability is a painful problem for PCM in past decades. As the semiconductor process technology rapidly scales down over the years, DRAM reaches 10 nm class process technology. In addition, it is reported that the write disturbance error (WDE) would be a serious issue for PCM if it scales down below 54 nm class process technology. Therefore, addressing the problem of WDEs becomes essential to make PCM competitive to DRAM. To overcome this problem, this dissertation proposes a novel approach that can restore meta-stable cells on demand by levering two-level SRAM-based tables, thereby significantly reducing the number WDEs. Furthermore, a novel randomized approach is proposed to implement a replacement policy that originally requires hundreds of read ports on SRAM. The second problem of PCM is a long-latency compared to that of DRAM. In particular, PCM tries to enhance its throughput by adopting a larger transaction unit; however, the different unit size from the general-purpose processor cache line further degrades the system performance due to the introduction of a read-modify-write (RMW) module. Since there has never been any research related to RMW in a PCM-based memory system, this dissertation proposes a novel architecture to enhance the overall system performance and reliability of a PCM-based memory system having an RMW module. The proposed architecture enhances data re-usability without introducing extra storage resources. Furthermore, a novel operation that merges commands regardless of command types is proposed to enhance performance notably. Another problem is the absence of a full simulation platform for PCM. While the announced features of the PCM-related product (i.e., Intel Optane) are scarce due to confidential issues, all priceless information can be integrated to develop an architecture simulator that resembles the available product. To this end, this dissertation tries to scrape up all available features of modules in a PCM controller and implement a dedicated simulator for future research purposes.상변화 메모리는(PCM) 매력적인 특성을 통해 메모리 시스템의 새로운 시대의 시작을 알렸다. 많은 메모리 관련 제품 제조업체(예 : 인텔, SK 하이닉스, 삼성)가 관련 제품 개발에 박차를 가하고 있다. PCM은 단순히 대규모 데이터베이스에만 국한되지 않고 다양한 상황에 적용될 수 있다. 예를 들어, PCM은 비휘발성으로 인해 대기 전력이 낮다. 따라서 계산 집약적인 애플리케이션 또는 모바일 애플리케이션은(즉, 긴 메모리 유휴 시간) PCM 기반 컴퓨팅 시스템에서 실행하기에 적합하다. PCM의 이러한 매력적인 특성에도 불구하고 PCM은 낮은 신뢰성과 긴 대기 시간으로 인해 여전히 일반 산업 시장에서는 DRAM과 다소 격차가 있다. 특히 낮은 신뢰성은 지난 수십 년 동안 PCM 기술의 발전을 저해하는 문제다. 반도체 공정 기술이 수년에 걸쳐 빠르게 축소됨에 따라 DRAM은 10nm 급 공정 기술에 도달하였다. 이어서, 쓰기 방해 오류 (WDE)가 54nm 등급 프로세스 기술 아래로 축소되면 PCM에 심각한 문제가 될 것으로 보고되었다. 따라서, WDE 문제를 해결하는 것은 PCM이 DRAM과 동등한 경쟁력을 갖추도록 하는 데 있어 필수적이다. 이 문제를 극복하기 위해 이 논문에서는 2-레벨 SRAM 기반 테이블을 활용하여 WDE 수를 크게 줄여 필요에 따라 준 안정 셀을 복원할 수 있는 새로운 접근 방식을 제안한다. 또한, 원래 SRAM에서 수백 개의 읽기 포트가 필요한 대체 정책을 구현하기 위해 새로운 랜덤 기반의 기법을 제안한다. PCM의 두 번째 문제는 DRAM에 비해 지연 시간이 길다는 것이다. 특히 PCM은 더 큰 트랜잭션 단위를 채택하여 단위시간 당 데이터 처리량 향상을 도모한다. 그러나 범용 프로세서 캐시 라인과 다른 유닛 크기는 읽기-수정-쓰기 (RMW) 모듈의 도입으로 인해 시스템 성능을 저하하게 된다. PCM 기반 메모리 시스템에서 RMW 관련 연구가 없었기 때문에 본 논문은 RMW 모듈을 탑재 한 PCM 기반 메모리 시스템의 전반적인 시스템 성능과 신뢰성을 향상하게 시킬 수 있는 새로운 아키텍처를 제안한다. 제안된 아키텍처는 추가 스토리지 리소스를 도입하지 않고도 데이터 재사용성을 향상시킨다. 또한, 성능 향상을 위해 명령 유형과 관계없이 명령을 병합하는 새로운 작업을 제안한다. 또 다른 문제는 PCM을 위한 완전한 시뮬레이션 플랫폼이 부재하다는 것이다. PCM 관련 제품(예 : Intel Optane)에 대해 발표된 정보는 대외비 문제로 인해 부족하다. 하지만 알려져 있는 정보를 적절히 취합하면 시중 제품과 유사한 아키텍처 시뮬레이터를 개발할 수 있다. 이를 위해 본 논문은 PCM 메모리 컨트롤러에 필요한 모든 모듈 정보를 활용하여 향후 이와 관련된 연구에서 충분히 사용 가능한 전용 시뮬레이터를 구현하였다.1 INTRODUCTION 1 1.1 Limitation of Traditional Main Memory Systems 1 1.2 Phase-Change Memory as Main Memory 3 1.2.1 Opportunities of PCM-based System 3 1.2.2 Challenges of PCM-based System 4 1.3 Dissertation Overview 7 2 BACKGROUND AND PREVIOUS WORK 8 2.1 Phase-Change Memory 8 2.2 Mitigation Schemes for Write Disturbance Errors 10 2.2.1 Write Disturbance Errors 10 2.2.2 Verification and Correction 12 2.2.3 Lazy Correction 13 2.2.4 Data Encoding-based Schemes 14 2.2.5 Sparse-Insertion Write Cache 16 2.3 Performance Enhancement for Read-Modify-Write 17 2.3.1 Traditional Read-Modify-Write 17 2.3.2 Write Coalescing for RMW 19 2.4 Architecture Simulators for PCM 21 2.4.1 NVMain 21 2.4.2 Ramulator 22 2.4.3 DRAMsim3 22 3 IN-MODULE DISTURBANCE BARRIER 24 3.1 Motivation 25 3.2 IMDB: In Module-Disturbance Barrier 29 3.2.1 Architectural Overview 29 3.2.2 Implementation of Data Structures 30 3.2.3 Modification of Media Controller 36 3.3 Replacement Policy 38 3.3.1 Replacement Policy for IMDB 38 3.3.2 Approximate Lowest Number Estimator 40 3.4 Putting All Together: Case Studies 43 3.5 Evaluation 45 3.5.1 Configuration 45 3.5.2 Architectural Exploration 47 3.5.3 Effectiveness of the Replacement Policy 48 3.5.4 Sensitivity to Main Table Configuration 49 3.5.5 Sensitivity to Barrier Buffer Size 51 3.5.6 Sensitivity to AppLE Group Size 52 3.5.7 Comparison with Other Studies 54 3.6 Discussion 59 3.7 Summary 63 4 INTEGRATION OF AN RMW MODULE IN A PCM-BASED SYSTEM 64 4.1 Motivation 65 4.2 Utilization of DRAM Cache for RMW 67 4.2.1 Architectural Design 67 4.2.2 Algorithm 70 4.3 Typeless Command Merging 73 4.3.1 Architectural Design 73 4.3.2 Algorithm 74 4.4 An Alternative Implementation: SRC-RMW 78 4.4.1 Implementation of SRC-RMW 78 4.4.2 Design Constraint 80 4.5 Case Study 82 4.6 Evaluation 85 4.6.1 Configuration 85 4.6.2 Speedup 88 4.6.3 Read Reliability 91 4.6.4 Energy Consumption: Selecting a Proper Page Size 93 4.6.5 Comparison with Other Studies 95 4.7 Discussion 97 4.8 Summary 99 5 AN ALL-INCLUSIVE SIMULATOR FOR A PCM CONTROLLER 100 5.1 Motivation 101 5.2 PCMCsim: PCM Controller Simulator 103 5.2.1 Architectural Overview 103 5.2.2 Underlying Classes of PCMCsim 104 5.2.3 Implementation of Contention Behavior 108 5.2.4 Modules of PCMCsim 109 5.3 Evaluation 116 5.3.1 Correctness of the Simulator 116 5.3.2 Comparison with Other Simulators 117 5.4 Summary 119 6 Conclusion 120 Abstract (In Korean) 141 Acknowledgment 143박

Improving Phase Change Memory (PCM) and Spin-Torque-Transfer Magnetic-RAM (STT-MRAM) as Next-Generation Memories: A Circuit Perspective

In the memory hierarchy of computer systems, the traditional semiconductor memories Static RAM (SRAM) and Dynamic RAM (DRAM) have already served for several decades as cache and main memory. With technology scaling, they face increasingly intractable challenges like power, density, reliability and scalability. As a result, they become less appealing in the multi/many-core era with ever increasing size and memory-intensity of working sets. Recently, there is an increasing interest in using emerging non-volatile memory technologies in replacement of SRAM and DRAM, due to their advantages like non-volatility, high device density, near-zero cell leakage and resilience to soft errors. Among several new memory technologies, Phase Change Memory (PCM) and Spin-Torque-Transfer Magnetic-RAM (STT-MRAM) are most promising candidates in building main memory and cache, respectively. However, both of them possess unique limitations that preventing them from being effectively adopted. In this dissertation, I present my circuit design work on tackling the limitations of PCM and STT-MRAM. At bit level, both PCM and STT-MRAM suffer from excessive write energy, and PCM has very limited write endurance. For PCM, I implement Differential Write to remove large number of unnecessary bit-writes that do not alter the stored data. It is then extended to STT-MRAM as Early Write Termination, with specific optimizations to eliminate the overhead of pre-write read. At array level, PCM enjoys high density but could not provide competitive throughput due to its long write latency and limited number of read/write circuits. I propose a Pseudo-Multi-Port Bank design to exploit intra-bank parallelism by recycling and reusing shared peripheral circuits between accesses in a time-multiplexed manner. On the other hand, although STT-MRAM features satisfactory throughput, its conventional array architecture is constrained on density and scalability by the pitch of the per-column bitline pair. I propose a Common-Source-Line Array architecture which uses a shared source-line along the row, essentially leaving only one bitline per column. For these techniques, I provide circuit level analyses as well as architecture/system level and/or process/device level discussions. In addition, relevant background and work are thoroughly surveyed and potential future research topics are discussed, offering insights and prospects of these next-generation memories

Dynamically parallel CAMSHIFT: GPU accelerated object tracking in digital video

The CAMSHIFT algorithm is widely used for tracking dynamically sized and positioned objects in real-time applications. In spite of its extensive study on the platform of sequential CPU, its research on massively parallel Graphical Processing Unit (GPU) platform is quite limited. In this work, we designed and implemented two different parallel algorithms for CAMSHIFT using CUDA. The first design performs calculations on the GPU, but requires iterative data transfers back to the host CPU for condition checking, which bottlenecks the entire program. In the second design, we propose an enhanced parallel reduction-based CAMSHIFT using dynamic parallelism to reduce overhead of data transfers between the CPU and GPU. Test results for a 400 by 400 search window show that the second design is up to five times faster than the first design and nine times faster than a pure CPU implementation. We also investigate the deployment of dynamic parallelism for multiple object tracking using CAMSHIFT --Leaf iv

Semiconductor Memory Applications in Radiation Environment, Hardware Security and Machine Learning System

abstract: Semiconductor memory is a key component of the computing systems. Beyond the conventional memory and data storage applications, in this dissertation, both mainstream and eNVM memory technologies are explored for radiation environment, hardware security system and machine learning applications. In the radiation environment, e.g. aerospace, the memory devices face different energetic particles. The strike of these energetic particles can generate electron-hole pairs (directly or indirectly) as they pass through the semiconductor device, resulting in photo-induced current, and may change the memory state. First, the trend of radiation effects of the mainstream memory technologies with technology node scaling is reviewed. Then, single event effects of the oxide based resistive switching random memory (RRAM), one of eNVM technologies, is investigated from the circuit-level to the system level. Physical Unclonable Function (PUF) has been widely investigated as a promising hardware security primitive, which employs the inherent randomness in a physical system (e.g. the intrinsic semiconductor manufacturing variability). In the dissertation, two RRAM-based PUF implementations are proposed for cryptographic key generation (weak PUF) and device authentication (strong PUF), respectively. The performance of the RRAM PUFs are evaluated with experiment and simulation. The impact of non-ideal circuit effects on the performance of the PUFs is also investigated and optimization strategies are proposed to solve the non-ideal effects. Besides, the security resistance against modeling and machine learning attacks is analyzed as well. Deep neural networks (DNNs) have shown remarkable improvements in various intelligent applications such as image classification, speech classification and object localization and detection. Increasing efforts have been devoted to develop hardware accelerators. In this dissertation, two types of compute-in-memory (CIM) based hardware accelerator designs with SRAM and eNVM technologies are proposed for two binary neural networks, i.e. hybrid BNN (HBNN) and XNOR-BNN, respectively, which are explored for the hardware resource-limited platforms, e.g. edge devices.. These designs feature with high the throughput, scalability, low latency and high energy efficiency. Finally, we have successfully taped-out and validated the proposed designs with SRAM technology in TSMC 65 nm. Overall, this dissertation paves the paths for memory technologies’ new applications towards the secure and energy-efficient artificial intelligence system.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Doctor of Philosophy

dissertationIn recent years, a number of trends have started to emerge, both in microprocessor and application characteristics. As per Moore's law, the number of cores on chip will keep doubling every 18-24 months. International Technology Roadmap for Semiconductors (ITRS) reports that wires will continue to scale poorly, exacerbating the cost of on-chip communication. Cores will have to navigate an on-chip network to access data that may be scattered across many cache banks. The number of pins on the package, and hence available off-chip bandwidth, will at best increase at sublinear rate and at worst, stagnate. A number of disruptive memory technologies, e.g., phase change memory (PCM) have begun to emerge and will be integrated into the memory hierarchy sooner than later, leading to non-uniform memory access (NUMA) hierarchies. This will make the cost of accessing main memory even higher. In previous years, most of the focus has been on deciding the memory hierarchy level where data must be placed (L1 or L2 caches, main memory, disk, etc.). However, in modern and future generations, each level is getting bigger and its design is being subjected to a number of constraints (wire delays, power budget, etc.). It is becoming very important to make an intelligent decision about where data must be placed within a level. For example, in a large non-uniform access cache (NUCA), we must figure out the optimal bank. Similarly, in a multi-dual inline memory module (DIMM) non uniform memory access (NUMA) main memory, we must figure out the DIMM that is the optimal home for every data page. Studies have indicated that heterogeneous main memory hierarchies that incorporate multiple memory technologies are on the horizon. We must develop solutions for data management that take heterogeneity into account. For these memory organizations, we must again identify the appropriate home for data. In this dissertation, we attempt to verify the following thesis statement: "Can low-complexity hardware and OS mechanisms manage data placement within each memory hierarchy level to optimize metrics such as performance and/or throughput?" In this dissertation we argue for a hardware-software codesign approach to tackle the above mentioned problems at different levels of the memory hierarchy. The proposed methods utilize techniques like page coloring and shadow addresses and are able to handle a large number of problems ranging from managing wire-delays in large, shared NUCA caches to distributing shared capacity among different cores. We then examine data-placement issues in NUMA main memory for a many-core processor with a moderate number of on-chip memory controllers. Using codesign approaches, we achieve efficient data placement by modifying the operating system's (OS) page allocation algorithm for a wide variety of main memory architectures