Efficient Exploration of Bus-Based System-on-Chip Architectures

Separation between computation and communication in system design allows system designers to explore the communication architecture independently after component selection and mapping decision is made. In this paper, we present an iterative two-step exploration methodology for bus-based on-chip communication architecture for multitask applications. We assume that the memory traces from the processing components are given. The proposed methodology uses a static performance estimation technique extended for multitask applications to reduce the design space quickly and drastically and applies a trace-driven simulation to the reduced set of design candidates for accurate performance estimation. For the case that local memory traffics as well as shared memory traffics are involved in bus contention, memory allocation is considered as an important axis of the design space in our technique. Experimental results show that the proposed methodology achieves significant performance gain by optimizing on-chip communication only, up to almost 100% compared with an initial single shared bus architecture, in both two real-life examples, a four-Channel digital video recorder and an equalizer for OFDM DVB-T receiverThis work was supported by the National Research Laboratory Program under Grant M1-0104-00-0015 and the IT Leading Research and Development Support Project funded by Korean MIC

Offline compression for on-chip RAM

ManuscriptWe present offline RAM compression, an automated source-to-source transformation that reduces a program's data size. Statically allocated scalars, pointers, structures, and arrays are encoded and packed based on the results of a whole-program analysis in the value set and pointer set domains. We target embedded software written in C that relies heavily on static memory allocation and runs on Harvard-architecture microcontrollers supporting just a few KB of on-chip RAM. On a collection of embedded applications for AVR microcontrollers, our transformation reduces RAM usage by an average of 12%, in addition to a 10% reduction through a dead-data elimination pass that is also driven by our whole-program analysis, for a total RAM savings of 22%. We also developed a technique for giving developers access to a flexible spectrum of tradeoffs between RAM consumption, ROM consumption, and CPU efficiency. This technique is based on a model for estimating the cost/benefit ratio of compressing each variable and then selectively compressing only those variables that present a good value proposition in terms of the desired tradeoffs

Energy-aware synthesis for networks on chip architectures

The Network on Chip (NoC) paradigm was introduced as a scalable communication infrastructure for future System-on-Chip applications. Designing application specific customized communication architectures is critical for obtaining low power, high performance solutions. Two significant design automation problems are the creation of an optimized configuration, given application requirement the implementation of this on-chip network. Automating the design of on-chip networks requires models for estimating area and energy, algorithms to effectively explore the design space and network component libraries and tools to generate the hardware description. Chip architects are faced with managing a wide range of customization options for individual components, routers and topology. As energy is of paramount importance, the effectiveness of any custom NoC generation approach lies in the availability of good energy models to effectively explore the design space. This thesis describes a complete NoC synthesis flow, called NoCGEN, for creating energy-efficient custom NoC architectures. Three major automation problems are addressed: custom topology generation, energy modeling and generation. An iterative algorithm is proposed to generate application specific point-to-point and packet-switched networks. The algorithm explores the design space for efficient topologies using characterized models and a system-level floorplanner for evaluating placement and wire-energy. Prior to our contribution, building an energy model required careful analysis of transistor or gate implementations. To alleviate the burden, an automated linear regression-based methodology is proposed to rapidly extract energy models for many router designs. The resulting models are cycle accurate with low-complexity and found to be within 10% of gate-level energy simulations, and execute several orders of magnitude faster than gate-level simulations. A hardware description of the custom topology is generated using a parameterizable library and custom HDL generator. Fully reusable and scalable network components (switches, crossbars, arbiters, routing algorithms) are described using a template approach and are used to compose arbitrary topologies. A methodology for building and composing routers and topologies using a template engine is described. The entire flow is implemented as several demonstrable extensible tools with powerful visualization functionality. Several experiments are performed to demonstrate the design space exploration capabilities and compare it against a competing min-cut topology generation algorithm

A hardware-software codesign framework for cellular computing

Until recently, the ever-increasing demand of computing power has been met on one hand by increasing the operating frequency of processors and on the other hand by designing architectures capable of exploiting parallelism at the instruction level through hardware mechanisms such as super-scalar execution. However, both these approaches seem to have reached a plateau, mainly due to issues related to design complexity and cost-effectiveness. To face the stabilization of performance of single-threaded processors, the current trend in processor design seems to favor a switch to coarser-grain parallelization, typically at the thread level. In other words, high computational power is achieved not only by a single, very fast and very complex processor, but through the parallel operation of several processors, each executing a different thread. Extrapolating this trend to take into account the vast amount of on-chip hardware resources that will be available in the next few decades (either through further shrinkage of silicon fabrication processes or by the introduction of molecular-scale devices), together with the predicted features of such devices (e.g., the impossibility of global synchronization or higher failure rates), it seems reasonable to foretell that current design techniques will not be able to cope with the requirements of next-generation electronic devices and that novel design tools and programming methods will have to be devised. A tempting source of inspiration to solve the problems implied by a massively parallel organization and inherently error-prone substrates is biology. In fact, living beings possess characteristics, such as robustness to damage and self-organization, which were shown in previous research as interesting to be implemented in hardware. For instance, it was possible to realize relatively simple systems, such as a self-repairing watch. Overall, these bio-inspired approaches seem very promising but their interest for a wider audience is problematic because their heavily hardware-oriented designs lack some of the flexibility achievable with a general purpose processor. In the context of this thesis, we will introduce a processor-grade processing element at the heart of a bio-inspired hardware system. This processor, based on a single-instruction, features some key properties that allow it to maintain the versatility required by the implementation of bio-inspired mechanisms and to realize general computation. We will also demonstrate that the flexibility of such a processor enables it to be evolved so it can be tailored to different types of applications. In the second half of this thesis, we will analyze how the implementation of a large number of these processors can be used on a hardware platform to explore various bio-inspired mechanisms. Based on an extensible platform of many FPGAs, configured as a networked structure of processors, the hardware part of this computing framework is backed by an open library of software components that provides primitives for efficient inter-processor communication and distributed computation. We will show that this dual software–hardware approach allows a very quick exploration of different ways to solve computational problems using bio-inspired techniques. In addition, we also show that the flexibility of our approach allows it to exploit replication as a solution to issues that concern standard embedded applications

Eliminating the call stack to save RAM

ManuscriptMost programming languages support a call stack in the programming model and also in the runtime system.We show that for applications targeting low-power embedded microcontrollers (MCUs), RAM usage can be significantly decreased by partially or completely eliminating the runtime callstack. We present flattening, a transformation that absorbs a function into its caller, replacing function invocations and returns with jumps. Unlike inlining, flattening does not duplicate the bodies of functions that have multiple callsites. Applied aggressively, flattening results in stack elimination. Flattening is most useful in conjunction with a lifting transformation that moves global variables into a local scope. Flattening and lifting can save RAM. However, even more benefit can be obtained by adapting the compiler to cope with properties of flattened code. First, we show that flattening adds false paths that confuse a standard live variables analysis. The resulting problems can be mitigated by breaking spurious live-range conflicts between variables using information from the unflattened callgraph. Second, we show that the impact of high register pressure due to flattened and lifted code, and consequent spills out of the register allocator, can be mitigated by improving a compiler's stack layout optimizations. We have implemented both of these improvements in GCC, and have implemented flattening and lifting as source-to-source transformations. On a collection of applications for the AVR family of 8-bit MCUs, we show that total RAM usage can be reduced by 20% by compiling flattened and lifted programs with our improved GCC

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

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