Ozone: Efficient Execution with Zero Timing Leakage for Modern Microarchitectures

Time variation during program execution can leak sensitive information. Time variations due to program control flow and hardware resource contention have been used to steal encryption keys in cipher implementations such as AES and RSA. A number of approaches to mitigate timing-based side-channel attacks have been proposed including cache partitioning, control-flow obfuscation and injecting timing noise into the outputs of code. While these techniques make timing-based side-channel attacks more difficult, they do not eliminate the risks. Prior techniques are either too specific or too expensive, and all leave remnants of the original timing side channel for later attackers to attempt to exploit. In this work, we show that the state-of-the-art techniques in timing side-channel protection, which limit timing leakage but do not eliminate it, still have significant vulnerabilities to timing-based side-channel attacks. To provide a means for total protection from timing-based side-channel attacks, we develop Ozone, the first zero timing leakage execution resource for a modern microarchitecture. Code in Ozone execute under a special hardware thread that gains exclusive access to a single core's resources for a fixed (and limited) number of cycles during which it cannot be interrupted. Memory access under Ozone thread execution is limited to a fixed size uncached scratchpad memory, and all Ozone threads begin execution with a known fixed microarchitectural state. We evaluate Ozone using a number of security sensitive kernels that have previously been targets of timing side-channel attacks, and show that Ozone eliminates timing leakage with minimal performance overhead

Memory Hierarchy Hardware-Software Co-design in Embedded Systems

The memory hierarchy is the main bottleneck in modern computer systems as the gap between the speed of the processor and the memory continues to grow larger. The situation in embedded systems is even worse. The memory hierarchy consumes a large amount of chip area and energy, which are precious resources in embedded systems. Moreover, embedded systems have multiple design objectives such as performance, energy consumption, and area, etc. Customizing the memory hierarchy for specific applications is a very important way to take full advantage of limited resources to maximize the performance. However, the traditional custom memory hierarchy design methodologies are phase-ordered. They separate the application optimization from the memory hierarchy architecture design, which tend to result in local-optimal solutions. In traditional Hardware-Software co-design methodologies, much of the work has focused on utilizing reconfigurable logic to partition the computation. However, utilizing reconfigurable logic to perform the memory hierarchy design is seldom addressed. In this paper, we propose a new framework for designing memory hierarchy for embedded systems. The framework will take advantage of the flexible reconfigurable logic to customize the memory hierarchy for specific applications. It combines the application optimization and memory hierarchy design together to obtain a global-optimal solution. Using the framework, we performed a case study to design a new software-controlled instruction memory that showed promising potential.Singapore-MIT Alliance (SMA

Power-Efficient and Low-Latency Memory Access for CMP Systems with Heterogeneous Scratchpad On-Chip Memory

The gradually widening speed disparity of between CPU and memory has become an overwhelming bottleneck for the development of Chip Multiprocessor (CMP) systems. In addition, increasing penalties caused by frequent on-chip memory accesses have raised critical challenges in delivering high memory access performance with tight power and latency budgets. To overcome the daunting memory wall and energy wall issues, this thesis focuses on proposing a new heterogeneous scratchpad memory architecture which is configured from SRAM, MRAM, and Z-RAM. Based on this architecture, we propose two algorithms, a dynamic programming and a genetic algorithm, to perform data allocation to different memory units, therefore reducing memory access cost in terms of power consumption and latency. Extensive and intensive experiments are performed to show the merits of the heterogeneous scratchpad architecture over the traditional pure memory system and the effectiveness of the proposed algorithms

Exploring Processor and Memory Architectures for Multimedia

Multimedia has become one of the cornerstones of our 21st century society and, when combined with mobility, has enabled a tremendous evolution of our society. However, joining these two concepts introduces many technical challenges. These range from having sufficient performance for handling multimedia content to having the battery stamina for acceptable mobile usage. When taking a projection of where we are heading, we see these issues becoming ever more challenging by increased mobility as well as advancements in multimedia content, such as introduction of stereoscopic 3D and augmented reality. The increased performance needs for handling multimedia come not only from an ongoing step-up in resolution going from QVGA (320x240) to Full HD (1920x1080) a 27x increase in less than half a decade. On top of this, there is also codec evolution (MPEG-2 to H.264 AVC) that adds to the computational load increase. To meet these performance challenges there has been processing and memory architecture advances (SIMD, out-of-order superscalarity, multicore processing and heterogeneous multilevel memories) in the mobile domain, in conjunction with ever increasing operating frequencies (200MHz to 2GHz) and on-chip memory sizes (128KB to 2-3MB). At the same time there is an increase in requirements for mobility, placing higher demands on battery-powered systems despite the steady increase in battery capacity (500 to 2000mAh). This leaves negative net result in-terms of battery capacity versus performance advances. In order to make optimal use of these architectural advances and to meet the power limitations in mobile systems, there is a need for taking an overall approach on how to best utilize these systems. The right trade-off between performance and power is crucial. On top of these constraints, the flexibility aspects of the system need to be addressed. All this makes it very important to reach the right architectural balance in the system. The first goal for this thesis is to examine multimedia applications and propose a flexible solution that can meet the architectural requirements in a mobile system. Secondly, propose an automated methodology of optimally mapping multimedia data and instructions to a heterogeneous multilevel memory subsystem. The proposed methodology uses constraint programming for solving a multidimensional optimization problem. Results from this work indicate that using today’s most advanced mobile processor technology together with a multi-level heterogeneous on-chip memory subsystem can meet the performance requirements for handling multimedia. By utilizing the automated optimal memory mapping method presented in this thesis lower total power consumption can be achieved, whilst performance for multimedia applications is improved, by employing enhanced memory management. This is achieved through reduced external accesses and better reuse of memory objects. This automatic method shows high accuracy, up to 90%, for predicting multimedia memory accesses for a given architecture

Scratchpad Management in Software Managed Manycore Architectures

abstract: Caches have long been used to reduce memory access latency. However, the increased complexity of cache coherence brings significant challenges in processor design as the number of cores increases. While making caches scalable is still an important research problem, some researchers are exploring the possibility of a more power-efficient SRAM called scratchpad memories or SPMs. SPMs consume significantly less area, and are more energy-efficient per access than caches, and therefore make the design of on-chip memories much simpler. Unlike caches, which fetch data from memories automatically, an SPM requires explicit instructions for data transfers. SPM-only architectures are thus named as software managed manycore (SMM), since the data movements of such architectures rely on software. SMM processors have been widely used in different areas, such as embedded computing, network processing, or even high performance computing. While SMM processors provide a low-power platform, the hardware alone does not guarantee power efficiency, if applications on such processors deliver low performance. Efficient software techniques are therefore required. A big body of management techniques for SMM architectures are compiler-directed, as inserting data movement operations by hand forces programmers to trace flow of data, which can be error-prone and sometimes difficult if not impossible. This thesis develops compiler-directed techniques to manage data transfers for embedded applications on SMMs efficiently. The techniques analyze and find out the proper program points and insert data movement instructions accordingly. The techniques manage code, stack and heap data of applications, and reduce execution time by 14%, 52% and 80% respectively compared to their predecessors on typical embedded applications. On top of managing local data, a technique is also developed for shared data in SMM architectures. Experimental results show it achieves more than 2X speedup than the previous technique on average.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Optimizing Heap Data Management on Software Managed Manycore Architectures

abstract: Caches pose a serious limitation in scaling many-core architectures since the demand of area and power for maintaining cache coherence increases rapidly with the number of cores. Scratch-Pad Memories (SPMs) provide a cheaper and lower power alternative that can be used to build a more scalable many-core architecture. The trade-off of substituting SPMs for caches is however that the data must be explicitly managed in software. Heap management on SPM poses a major challenge due to the highly dynamic nature of of heap data access. Most existing heap management techniques implement a software caching scheme on SPM, emulating the behavior of hardware caches. The state-of-the-art heap management scheme implements a 4-way set-associative software cache on SPM for a single program running with one thread on one core. While the technique works correctly, it suffers from signifcant performance overhead. This paper presents a series of compiler-based efficient heap management approaches that reduces heap management overhead through several optimization techniques. Experimental results on benchmarks from MiBenchGuthaus et al. (2001) executed on an SMM processor modeled in gem5Binkert et al. (2011) demonstrate that our approach (implemented in llvm v3.8Lattner and Adve (2004)) can improve execution time by 80% on average compared to the previous state-of-the-art.Dissertation/ThesisMasters Thesis Computer Science 201

Embedded Systems Energy Characterization using non-Intrusive Instrumentation

Research Report RR2006-37, LIP - ENS LyonThis research report presents a non intrusive methodology for building embedded systems energy consumption models. The method is based on measurement on real hardware in order to get a quantitative approach that takes into account the full architecture. Based on these measurements, data are grouped into class of instructions and events. These classes can then be reused in software simulators and in high-level source code transformation cost functions for optimizing compilers. The computed power model is much more simpler than previous power models while being accurate at the platform level. The methodology is illustrated using experimental results made on an ARM Integrator platform for which an accurate and full system energy model is build

A composable, energy-managed, real-time MPSOC platform.

Multi-processors systems on chip (MPSOC) platforms emerged in embedded systems as hardware solutions to support the continuously increasing functionality and performance demands in this domain. Such a platform has to execute a mix of applications with diverse performance and timing constraints, i.e., real-time or non-real-time, thus different application schedulers should co-exist on an MPSOC. Moreover, applications share many MPSOC resources, thus their timing depends on the arbitration at these resources. Arbitration may create inter-application dependencies, e.g., the timing of a low priority application depends on the timing of all higher priority ones. Application inter-dependencies make the functional and timing verification and the integration process harder. This is especially problematic for real-time applications, for which fulfilling the time-related constraints should be guaranteed by construction. Moreover, energy and power management, commonly employed in embedded systems, make this verification even more difficult. Typically, energy and power management involves scaling the resources operating point, which has a direct impact on the resource performance, thus influences the application time behaviour. Finally, a small change in one application leads to the need to re-verify all other applications, incurring a large effort. Composability is a property meant to ease the verification and integration process. A system is composable if the functionality and the timing behaviour of each application is independent of other applications mapped on the same platform. Composability is achieved by utilising arbiters that ensure applications independence. In this paper we present the concepts behind a composable, scalable, energy-managed MPSOC platform, able to support different real-time and nonreal time schedulers concurrently, and discuss its advantages and limitations