Power-Performance Modeling and Adaptive Management of Heterogeneous Mobile Platforms​

abstract: Nearly 60% of the world population uses a mobile phone, which is typically powered by a system-on-chip (SoC). While the mobile platform capabilities range widely, responsiveness, long battery life and reliability are common design concerns that are crucial to remain competitive. Consequently, state-of-the-art mobile platforms have become highly heterogeneous by combining a powerful SoC with numerous other resources, including display, memory, power management IC, battery and wireless modems. Furthermore, the SoC itself is a heterogeneous resource that integrates many processing elements, such as CPU cores, GPU, video, image, and audio processors. Therefore, CPU cores do not dominate the platform power consumption under many application scenarios. Competitive performance requires higher operating frequency, and leads to larger power consumption. In turn, power consumption increases the junction and skin temperatures, which have adverse effects on the device reliability and user experience. As a result, allocating the power budget among the major platform resources and temperature control have become fundamental consideration for mobile platforms. Dynamic thermal and power management algorithms address this problem by putting a subset of the processing elements or shared resources to sleep states, or throttling their frequencies. However, an adhoc approach could easily cripple the performance, if it slows down the performance-critical processing element. Furthermore, mobile platforms run a wide range of applications with time varying workload characteristics, unlike early generations, which supported only limited functionality. As a result, there is a need for adaptive power and performance management approaches that consider the platform as a whole, rather than focusing on a subset. Towards this need, our specific contributions include (a) a framework to dynamically select the Pareto-optimal frequency and active cores for the heterogeneous CPUs, such as ARM big.Little architecture, (b) a dynamic power budgeting approach for allocating optimal power consumption to the CPU and GPU using performance sensitivity models for each PE, (c) an adaptive GPU frame time sensitivity prediction model to aid power management algorithms, and (d) an online learning algorithm that constructs adaptive run-time models for non-stationary workloads.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Doctor of Philosophy

dissertationThe embedded system space is characterized by a rapid evolution in the complexity and functionality of applications. In addition, the short time-to-market nature of the business motivates the use of programmable devices capable of meeting the conflicting constraints of low-energy, high-performance, and short design times. The keys to achieving these conflicting constraints are specialization and maximally extracting available application parallelism. General purpose processors are flexible but are either too power hungry or lack the necessary performance. Application-specific integrated circuits (ASICS) efficiently meet the performance and power needs but are inflexible. Programmable domain-specific architectures (DSAs) are an attractive middle ground, but their design requires significant time, resources, and expertise in a variety of specialties, which range from application algorithms to architecture and ultimately, circuit design. This dissertation presents CoGenE, a design framework that automates the design of energy-performance-optimal DSAs for embedded systems. For a given application domain and a user-chosen initial architectural specification, CoGenE consists of a a Compiler to generate execution binary, a simulator Generator to collect performance/energy statistics, and an Explorer that modifies the current architecture to improve energy-performance-area characteristics. The above process repeats automatically until the user-specified constraints are achieved. This removes or alleviates the time needed to understand the application, manually design the DSA, and generate object code for the DSA. Thus, CoGenE is a new design methodology that represents a significant improvement in performance, energy dissipation, design time, and resources. This dissertation employs the face recognition domain to showcase a flexible architectural design methodology that creates "ASIC-like" DSAs. The DSAs are instruction set architecture (ISA)-independent and achieve good energy-performance characteristics by coscheduling the often conflicting constraints of data access, data movement, and computation through a flexible interconnect. This represents a significant increase in programming complexity and code generation time. To address this problem, the CoGenE compiler employs integer linear programming (ILP)-based 'interconnect-aware' scheduling techniques for automatic code generation. The CoGenE explorer employs an iterative technique to search the complete design space and select a set of energy-performance-optimal candidates. When compared to manual designs, results demonstrate that CoGenE produces superior designs for three application domains: face recognition, speech recognition and wireless telephony. While CoGenE is well suited to applications that exhibit a streaming behavior, multithreaded applications like ray tracing present a different but important challenge. To demonstrate its generality, CoGenE is evaluated in designing a novel multicore N-wide SIMD architecture, known as StreamRay, for the ray tracing domain. CoGenE is used to synthesize the SIMD execution cores, the compiler that generates the application binary, and the interconnection subsystem. Further, separating address and data computations in space reduces data movement and contention for resources, thereby significantly improving performance compared to existing ray tracing approaches

성능과 용량 향상을 위한 적층형 메모리 구조

학위논문 (박사)-- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2019. 2. 안정호.The advance of DRAM manufacturing technology slows down, whereas the density and performance needs of DRAM continue to increase. This desire has motivated the industry to explore emerging Non-Volatile Memory (e.g., 3D XPoint) and the high-density DRAM (e.g., Managed DRAM Solution). Since such memory technologies increase the density at the cost of longer latency, lower bandwidth, or both, it is essential to use them with fast memory (e.g., conventional DRAM) to which hot pages are transferred at runtime. Nonetheless, we observe that page transfers to fast memory often block memory channels from servicing memory requests from applications for a long period. This in turn significantly increases the high-percentile response time of latency-sensitive applications. In this thesis, we propose a high-density managed DRAM architecture, dubbed 3D-XPath for applications demanding both low latency and high capacity for memory. 3D-XPath DRAM stacks conventional DRAM dies with high-density DRAM dies explored in this thesis and connects these DRAM dies with 3D-XPath. Especially, 3D-XPath allows unused memory channels to service memory requests from applications when primary channels supposed to handle the memory requests are blocked by page transfers at given moments, considerably increasing the high-percentile response time. This can also improve the throughput of applications frequently copying memory blocks between kernel and user memory spaces. Our evaluation shows that 3D-XPath DRAM decreases high-percentile response time of latency-sensitive applications by ∼30% while improving the throughput of an I/O-intensive applications by ∼39%, compared with DRAM without 3D-XPath. Recent computer systems are evolving toward the integration of more CPU cores into a single socket, which require higher memory bandwidth and capacity. Increasing the number of channels per socket is a common solution to the bandwidth demand and to better utilize these increased channels, data bus width is reduced and burst length is increased. However, this longer burst length brings increased DRAM access latency. On the memory capacity side, process scaling has been the answer for decades, but cell capacitance now limits how small a cell could be. 3D stacked memory solves this problem by stacking dies on top of other dies. We made a key observation in real multicore machine that multiple memory controllers are always not fully utilized on SPEC CPU 2006 rate benchmark. To bring these idle channels into play, we proposed memory channel sharing architecture to boost peak bandwidth of one memory channel and reduce the burst latency on 3D stacked memory. By channel sharing, the total performance on multi-programmed workloads and multi-threaded workloads improved up to respectively 4.3% and 3.6% and the average read latency reduced up to 8.22% and 10.18%.DRAM 제조 기술의 발전은 속도가 느려지는 반면 DRAM의 밀도 및 성능 요구는 계속 증가하고 있다. 이러한 요구로 인해 새로운 비 휘발성 메모리(예: 3D-XPoint) 및 고밀도 DRAM(예: Managed asymmetric latency DRAM Solution)이 등장하였다. 이러한 고밀도 메모리 기술은 긴 레이턴시, 낮은 대역폭 또는 두 가지 모두를 사용하는 방식으로 밀도를 증가시키기 때문에 성능이 좋지 않아, 핫 페이지를 고속 메모리(예: 일반 DRAM)로 스왑되는 저용량의 고속 메모리가 동시에 사용되는 것이 일반적이다. 이러한 스왑 과정에서 빠른 메모리로의 페이지 전송이 일반적인 응용프로그램의 메모리 요청을 오랫동안 처리하지 못하도록 하기 때문에, 대기 시간에 민감한 응용 프로그램의 백분위 응답 시간을 크게 증가시켜, 응답 시간의 표준 편차를 증가시킨다. 이러한 문제를 해결하기 위해 본 학위 논문에서는 저 지연시간 및 고용량 메모리를 요구하는 애플리케이션을 위해 3D-XPath, 즉 고밀도 관리 DRAM 아키텍처를 제안한다. 이러한 3D-톔소를 집적한 DRAM은 저속의 고밀도 DRAM 다이를 기존의 일반적인 DRAM 다이와 동시에 한 칩에 적층하고, DRAM 다이끼리는 제안하는 3D-XPath 하드웨어를 통해 연결된다. 이러한 3D-XPath는 핫 페이지 스왑이 일어나는 동안 응용프로그램의 메모리 요청을 차단하지 않고 사용량이 적은 메모리 채널로 핫 페이지 스왑을 처리 할 수 있도록 하여, 데이터 집중 응용 프로그램의 백분위 응답 시간을 개선시킨다. 또한 제안하는 하드웨어 구조를 사용하여, 추가적으로 O/S 커널과 유저 스페이스 간의 메모리 블록을 자주 복사하는 응용 프로그램의 처리량을 향상시킬 수 있다. 이러한 3D-XPath DRAM은 3D-XPath가 없는 DRAM에 비해 I/O 집약적인 응용프로그램의 처리량을 최대 39 % 향상시키면서 레이턴시에 민감한 응용 프로그램의 높은 백분위 응답 시간을 최대 30 %까지 감소시킬 수 있다. 또한 최근의 컴퓨터 시스템은 보다 많은 메모리 대역폭과 용량을 필요로하는 더 많은 CPU 코어를 단일 소켓으로 통합하는 방향으로 진화하고 있다. 이러한 소켓 당 채널 수를 늘리는 것은 대역폭 요구에 대한 일반적인 해결책이며, 최신의 DRAM 인터페이스의 발전 양상은 증가한 채널을 보다 잘 활용하기 위해 데이터 버스 폭이 감소되고 버스트 길이가 증가한다. 그러나 길어진 버스트 길이는 DRAM 액세스 대기 시간을 증가시킨다. 추가적으로 최신의 응용프로그램은 더 많은 메모리 용량을 요구하며, 미세 공정으로 메모리 용량을 증가시키는 방법론은 수십 년 동안 사용되었지만, 20 nm 이하의 미세공정에서는 더 이상 공정 미세화를 통해 메모리 밀도를 증가시키기가 어려운 상황이며, 적층형 메모리를 사용하여 용량을 증가시키는 방법을 사용한다. 이러한 상황에서, 실제 최신의 멀티코어 머신에서 SPEC CPU 2006 응용프로그램을 멀티코어에서 실행하였을 때, 항상 시스템의 모든 메모리 컨트롤러가 완전히 활용되지 않는다는 사실을 관찰했다. 이러한 유휴 채널을 사용하기 위해 하나의 메모리 채널의 피크 대역폭을 높이고 3D 스택 메모리의 버스트 대기 시간을 줄이기 위해 본 학위 논문에서는 메모리 채널 공유 아키텍처를 제안하였으며, 하드웨어 블록을 제안하였다. 이러한 채널 공유를 통해 멀티 프로그램 된 응용프로그램 및 다중 스레드 응용프로그램 성능이 각각 4.3 % 및 3.6 %로 향상되었으며 평균 읽기 대기 시간은 8.22 % 및 10.18 %로 감소하였다.Contents Abstract i Contents iv List of Figures vi List of Tables viii Introduction 1 1.1 3D-XPath: High-Density Managed DRAM Architecture with Cost-effective Alternative Paths for Memory Transactions 5 1.2 Boosting Bandwidth – Dynamic Channel Sharing on 3D Stacked Memory 9 1.3 Research contribution 13 1.4 Outline 14 3D-stacked Heterogeneous Memory Architecture with Cost-effective Extra Block Transfer Paths 17 2.1 Background 17 2.1.1 Heterogeneous Main Memory Systems 17 2.1.2 Specialized DRAM 19 2.1.3 3D-stacked Memory 22 2.2 HIGH-DENSITY DRAM ARCHITECTURE 27 2.2.1 Key Design Challenges 29 2.2.2 Plausible High-density DRAM Designs 33 2.3 3D-STACKED DRAM WITH ALTERNATIVE PATHS FOR MEMORY TRANSACTIONS 37 2.3.1 3D-XPath Architecture 41 2.3.2 3D-XPath Management 46 2.4 EXPERIMENTAL METHODOLOGY 52 2.5 EVALUATION 56 2.5.1 OLDI Workloads 56 2.5.2 Non-OLDI Workloads 61 2.5.3 Sensitivity Analysis 66 2.6 RELATED WORK 70 Boosting bandwidth –Dynamic Channel Sharing on 3D Stacked Memory 72 3.1 Background: Memory Operations 72 3.1.1. Memory Controller 72 3.1.2 DRAM column access sequence 73 3.2 Related Work 74 3.3. CHANNEL SHARING ENABLED MEMORY SYSTEM 76 3.3.1 Hardware Requirements 78 3.3.2 Operation Sequence 81 3.4 Analysis 87 3.4.1 Experiment Environment 87 3.4.2 Performance 88 3.4.3 Overhead 90 CONCLUSION 92 REFERENCES 94 국문초록 107Docto

DeepNVM++: Cross-Layer Modeling and Optimization Framework of Non-Volatile Memories for Deep Learning

Non-volatile memory (NVM) technologies such as spin-transfer torque magnetic random access memory (STT-MRAM) and spin-orbit torque magnetic random access memory (SOT-MRAM) have significant advantages compared to conventional SRAM due to their non-volatility, higher cell density, and scalability features. While previous work has investigated several architectural implications of NVM for generic applications, in this work we present DeepNVM++, a framework to characterize, model, and analyze NVM-based caches in GPU architectures for deep learning (DL) applications by combining technology-specific circuit-level models and the actual memory behavior of various DL workloads. We present both iso-capacity and iso-area performance and energy analysis for systems whose last-level caches rely on conventional SRAM and emerging STT-MRAM and SOT-MRAM technologies. In the iso-capacity case, STT-MRAM and SOT-MRAM provide up to 3.8x and 4.7x energy-delay product (EDP) reduction and 2.4x and 2.8x area reduction compared to conventional SRAM, respectively. Under iso-area assumptions, STT-MRAM and SOT-MRAM provide up to 2x and 2.3x EDP reduction and accommodate 2.3x and 3.3x cache capacity when compared to SRAM, respectively. We also perform a scalability analysis and show that STT-MRAM and SOT-MRAM achieve orders of magnitude EDP reduction when compared to SRAM for large cache capacities. Our comprehensive cross-layer framework is demonstrated on STT-/SOT-MRAM technologies and can be used for the characterization, modeling, and analysis of any NVM technology for last-level caches in GPUs for DL applications.Comment: 12 pages, 10 figure