Compilation and Scheduling Techniques for Embedded Systems

Embedded applications are constantly increasing in size, which has resulted in increasing demand on designers of digital signal processors (DSPs) to meet the tight memory, size and cost constraints. With this trend, memory requirement reduction through code compaction and variable coalescing techniques are gaining more ground. Also, as the current trend in complex embedded systems of using multiprocessor system-on-chip (MPSoC) grows, problems like mapping, memory management and scheduling are gaining more attention. The first part of the dissertation deals with problems related to digital signal processors. Most modern DSPs provide multiple address registers and a dedicated address generation unit (AGU) which performs address generation in parallel to instruction execution. A careful placement of variables in memory is important in decreasing the number of address arithmetic instructions leading to compact and efficient code. Chapters 2 and 3 present effective heuristics for the simple and the general offset assignment problems with variable coalescing. A solution based on simulated annealing is also presented. Chapter 4 presents an optimal integer linear programming (ILP) solution to the offset assignment problem with variable coalescing and operand permutation. A new approach to the general offset assignment problem is introduced. Chapter 5 presents an optimal ILP formulation and a genetic algorithm solution to the address register allocation problem (ARA) with code transformation techniques. The ARA problem is used to generate compact codes for array-intensive embedded applications. In the second part of the dissertation, we study problems related to MPSoCs. MPSoCs provide the flexibility to meet the performance requirements of multimedia applications while respecting the tight embedded system constraints. MPSoC-based embedded systems often employ software-managed memories called scratch-pad memories (SPM). Scheduling the tasks of an application on the processors and partitioning the available SPM budget among those processors are two critical issues in reducing the overall computation time. Traditionally, the step of task scheduling is applied separately from the memory partitioning step. Such a decoupled approach may miss better quality schedules. Chapters 6 and 7 present effective heuristics that integrate task allocation and SPM partitioning to further reduce the execution time of embedded applications for single and multi-application scenarios

Data locality and parallelism optimization using a constraint-based approach

Cataloged from PDF version of article.Embedded applications are becoming increasingly complex and processing ever-increasing datasets. In the context of data-intensive embedded applications, there have been two complementary approaches to enhancing application behavior, namely, data locality optimizations and improving loop-level parallelism. Data locality needs to be enhanced to maximize the number of data accesses satisfied from the higher levels of the memory hierarchy. On the other hand, compiler-based code parallelization schemes require a fresh look for chip multiprocessors as interprocessor communication is much cheaper than off-chip memory accesses. Therefore, a compiler needs to minimize the number of off-chip memory accesses. This can be achieved by considering multiple loop nests simultaneously. Although compilers address these two problems, there is an inherent difficulty in optimizing both data locality and parallelism simultaneously. Therefore, an integrated approach that combines these two can generate much better results than each individual approach. Based on these observations, this paper proposes a constraint network (CN)-based formulation for data locality optimization and code parallelization. The paper also presents experimental evidence, demonstrating the success of the proposed approach, and compares our results with those obtained through previously proposed approaches. The experiments from our implementation indicate that the proposed approach is very effective in enhancing data locality and parallelization. © 2010 Elsevier Inc. All rights reserved

NengoFPGA: an FPGA Backend for the Nengo Neural Simulator

Low-power, high-speed neural networks are critical for providing deployable embedded AI applications at the edge. We describe a Xilinx FPGA implementation of Neural Engineering Framework (NEF) networks with online learning that outperforms mobile Nvidia GPU implementations by an order of magnitude or more. Specifically, we provide an embedded Python-capable PYNQ FPGA implementation supported with a Xilinx Vivado High-Level Synthesis (HLS) workflow that allows sub-millisecond implementation of adaptive neural networks with low-latency, direct I/O access to the physical world. The outcome of this work is NengoFPGA, a seamless and user-friendly extension to the neural compiler Python package Nengo. To reduce memory requirements and improve performance we tune the precision of the different intermediate variables in the code to achieve competitive absolute accuracy against slower and larger floating-point reference designs. The online learning component of the neural network exploits immediate feedback to adjust the network weights to best support a given arithmetic precision. As the space of possible design configurations of such quantized networks is vast and is subject to a target accuracy constraint, we use the Hyperopt hyper-parameter tuning tool instead of manual search to find Pareto optimal designs. Specifically, we are able to generate the optimized designs in under 500 short iterations of Vivado HLS C synthesis before running the complete Vivado place-and-route phase on that subset, a much longer process not conducive to rapid exploration. For neural network populations of 64–4096 neurons and 1–8 representational dimensions our optimized FPGA implementation generated by Hyperopt has a speedup of 10–484× over a competing cuBLAS implementation on the Jetson TX1 GPU while using 2.4–9.5× less power. Our speedups are a result of HLS-specific reformulation (15× improvement), precision adaptation (3× improvement), and low-latency direct I/O access (1000× improvement)

Computer Assisted Design and Integration of FPGA Accelerators in Aerospace Systems

The integration of Field Programmable Gate Arrays (FPGAs) in an aerospace system allows to improve its efficiency and its flexibility thanks to their programmability. To exploit these devices, the designer has to identify the functionalities that have to be executed on them and provide their implementation by means of Hardware Description Languages. Generating these descriptions for a software developer could be a very difficult task because of the different programming paradigms of software programs and hardware descriptions. To facilitate the developer in this activity, High Level Synthesis techniques have been developed aiming at (semi-)automatically generating hardware implementations of specifications written in high level languages (e.g., C). State of the art tools implementing such methodologies have not been designed for the integration with aerospace systems design flows, so significant adaptations could be required to the designer for integrating the hardware implementations with the rest of the design solution. In this paper the integration of a High Level Synthesis design flow in the TASTE framework (http://taste.tuxfamily.org) is presented. TASTE is a set of freely available tools for the development of real time embedded systems developed by the European Space Agency together with a set of its industrial partners. This framework allows to integrate specifications described in different languages (e.g., C, ADA, Simulink, SDL) by means of formal languages (AADL and ASN.1) and to early verify the correctness of the produced solutions. TASTE has been extended with Bambu (http://panda.dei.polimi.it), a tool for the High Level Synthesis developed at Politecnico di Milano. In this way the TASTE users have the possibility to specify which functionalities, provided by means of high level languages such C, have to be implemented in hardware on the FPGA without having to directly provide the hardware implementations. Thanks to the integration of the High Level Synthesis tool indeed, the framework is able not only to produce the hardware implementations, but also to integrate them in the rest of the aerospace system by automatically generating the whole architecture to be implemented on the FPGA. This architecture contains not only the implementation of the hardware accelerators, but also of the components required to transfer the data from and to the rest of the system and to correctly manage their size and endianness. The application of the extended framework to a real case study shows its effective usability

ACCURATE: Accuracy Maximization for Real-Time Multi-core systems with Energy Efficient Way-sharing Caches

Improving result-accuracy in approximate computing (AC) based real-time applications without violating deadline has recently become an active research domain. Execution-time of AC real-time tasks can individually be separated into: execution of the mandatory part to obtain a result of acceptable quality, followed by a partial/complete execution of the optional part to improve result-accuracy of the initial result within a given deadline. However, obtaining higher result-accuracy at the cost of enhanced execution time may lead to deadline violation, along with higher energy usage.We present ACCURATE, a novel hybrid offline-online approximate real-time scheduling approach that first schedules AC-based tasks on multi-core with an objective to maximize result-accuracy and determines operational processing speeds for each task constrained by system-wide power limit, deadline, and task-dependency. At runtime, by employing a waysharing technique (WH LLC) at the last level cache, ACCURATE improves performance, which is further leveraged, to enhance result-accuracy by executing more from the optional part, and to improve energy efficiency of the cache by turning off a controlled number of cache-ways. ACCURATE also exploits the slacks either to improve result-accuracy of the tasks, or to enhance energy efficiency of the underlying system, or both. ACCURATE achieves 85% QoS with 36% average reduction in cache leakage consumption with a 24% average gain in energy delay product for a 4-core based chip-multiprocessor with 6.4% average improvement in performance

Computational methods and software systems for dynamics and control of large space structures

Two key areas of crucial importance to the computer-based simulation of large space structures are discussed. The first area involves multibody dynamics (MBD) of flexible space structures, with applications directed to deployment, construction, and maneuvering. The second area deals with advanced software systems, with emphasis on parallel processing. The latest research thrust in the second area involves massively parallel computers

Power and memory optimization techniques in embedded systems design

Embedded systems incur tight constraints on power consumption and memory (which impacts size) in addition to other constraints such as weight and cost. This dissertation addresses two key factors in embedded system design, namely minimization of power consumption and memory requirement. The first part of this dissertation considers the problem of optimizing power consumption (peak power as well as average power) in high-level synthesis (HLS). The second part deals with memory usage optimization mainly targeting a restricted class of computations expressed as loops accessing large data arrays that arises in scientific computing such as the coupled cluster and configuration interaction methods in quantum chemistry. First, a mixed-integer linear programming (MILP) formulation is presented for the scheduling problem in HLS using multiple supply-voltages in order to optimize peak power as well as average power and energy consumptions. For large designs, the MILP formulation may not be suitable; therefore, a two-phase iterative linear programming formulation and a power-resource-saving heuristic are presented to solve this problem. In addition, a new heuristic that uses an adaptation of the well-known force-directed scheduling heuristic is presented for the same problem. Next, this work considers the problem of module selection simultaneously with scheduling for minimizing peak and average power consumption. Then, the problem of power consumption (peak and average) in synchronous sequential designs is addressed. A solution integrating basic retiming and multiple-voltage scheduling (MVS) is proposed and evaluated. A two-stage algorithm namely power-oriented retiming followed by a MVS technique for peak and/or average power optimization is presented. Memory optimization is addressed next. Dynamic memory usage optimization during the evaluation of a special class of interdependent large data arrays is considered. Finally, this dissertation develops a novel integer-linear programming (ILP) formulation for static memory optimization using the well-known fusion technique by encoding of legality rules for loop fusion of a special class of loops using logical constraints over binary decision variables and a highly effective approximation of memory usage