Domain Specific Modeling (DSM) aims to increase productivity of software development by raising the level of abstraction beyond code concepts and using domain concepts. By providing a generative model-driven tooling component and runtime support, the Eclipse Graphical Modeling Framework (GMF) aims to simplify the creation of diagram editors for specific domains based on a series of model creation and transformation steps. GMF leverages the Eclipse Modeling Framework (EMF) and the Eclipse Graphical Editing Framework (GEF) to allow the graphical modeling of Domain Specific Languages (DSL). A Domain Specific Language (DSL) is developed specifically for a specific task and specific domain. In this research, the State Machine Compiler (SMC) represents the specific domain for which a DSL in a form of a diagram editor is developed using GMF. SMC is an open source Java tool allowing generation of state pattern classes from textual descriptions of state machines. The main objective of this research is to describe the use of GMF, highlight potential pitfalls and identify strengths and weaknesses of GMF based on certain criteria. To be able to feed the SMC diagrams created with the editor into SMC, a Java Emitter Templates (JET) transformation is used to transform SMC model instances into textual format expected by SMC. / Thesis (Master, Computing) -- Queen's University, 2011-04-14 18:58:08.797

Scratch-pad memory (SPM), a fast on-chip SRAM managed by software, is widely used in embedded systems. Compared to hardware-managed cache, SPM can be more efficient in performance, power and area cost, and has the added advantage of better time predictability. In this thesis, SPMs should be seen in a general context. For example, in stream processors, a software-managed stream register file is usually used to stage data to and from off-chip memory. In IBM's Cell architecture, each co-processor has a software-managed local store for keeping data and instructions. SPM management is critical for SPM-based embedded systems. In this thesis, we propose two novel methodologies, the memory colouring methodology and the perfect colouring methodology, to place the static data aggregates such as arrays and structs of a program in SPM. Our methodologies are dynamic in the sense that some data aggregates can be swapped into and out of SPM during program execution. To this end, a live range splitting heuristic is introduced in order to create potential data transfer statements between SPM and off-chip memory. The memory colouring methodology is a general-purpose compiler approach. The novelty of this approach lies in partitioning an SPM into a pseudo register file then generalising existing graph colouring algorithms for register allocation to colour data aggregates. In this thesis, a scheme for partitioning an SPM into a pseudo register file is introduced. This methodology is inter-procedural and therefore operates on the interference graph for the data aggregates in the whole program. Different graph colouring algorithms may give rise to different results due to live range splitting and spilling heuristics used. As a result, two representative graph colouring algorithms, George and Appel's iterative-coalescing and Park and Moon's optimistic-coalescing, are generalised and evaluated for SPM allocation. Like memory colouring, perfect colouring is also inter-procedural. The novelty of this second methodology lies in formulating the SPM allocation problem as an interval colouring problem. The interval colouring problem is an NP problem and no widely-accepted approximation algorithms exist. The key observation is that the interference graphs for data aggregates in many embedded applications form a special class of superperfect graphs. This has led to the development of two additional SPM allocation algorithms. While differing in whether live range splits and spills are done sequentially or together, both algorithms place data aggregates in SPM based on the cliques in an interference graph. In both cases, we guarantee optimally that all data aggregates in an interference graph can be placed in SPM if the given SPM size is no smaller than the chromatic number of the graph. We have developed two memory colouring algorithms and two perfect colouring algorithms for SPM allocation. We have evaluated them using a set of embedded applications. Our results show that both methodologies are efficient and effective in handling large-scale embedded applications. While neither methodology outperforms the other consistently, perfect colouring has yielded better overall results in the set of benchmarks used in our experiments. All these algorithms are expected to be valuable. For example, they can be made available as part of the same compiler framework to assist the embedded designer with exploring a large number of optimisation opportunities for a particular embedded application.

Scratch-pad memory (SPM), a fast on-chip SRAM managed by software, is widely used in embedded systems. Compared to hardware-managed cache, SPM can be more efficient in performance, power and area cost, and has the added advantage of better time predictability. In this thesis, SPMs should be seen in a general context. For example, in stream processors, a software-managed stream register file is usually used to stage data to and from off-chip memory. In IBM's Cell architecture, each co-processor has a software-managed local store for keeping data and instructions. SPM management is critical for SPM-based embedded systems. In this thesis, we propose two novel methodologies, the memory colouring methodology and the perfect colouring methodology, to place the static data aggregates such as arrays and structs of a program in SPM. Our methodologies are dynamic in the sense that some data aggregates can be swapped into and out of SPM during program execution. To this end, a live range splitting heuristic is introduced in order to create potential data transfer statements between SPM and off-chip memory. The memory colouring methodology is a general-purpose compiler approach. The novelty of this approach lies in partitioning an SPM into a pseudo register file then generalising existing graph colouring algorithms for register allocation to colour data aggregates. In this thesis, a scheme for partitioning an SPM into a pseudo register file is introduced. This methodology is inter-procedural and therefore operates on the interference graph for the data aggregates in the whole program. Different graph colouring algorithms may give rise to different results due to live range splitting and spilling heuristics used. As a result, two representative graph colouring algorithms, George and Appel's iterative-coalescing and Park and Moon's optimistic-coalescing, are generalised and evaluated for SPM allocation. Like memory colouring, perfect colouring is also inter-procedural. The novelty of this second methodology lies in formulating the SPM allocation problem as an interval colouring problem. The interval colouring problem is an NP problem and no widely-accepted approximation algorithms exist. The key observation is that the interference graphs for data aggregates in many embedded applications form a special class of superperfect graphs. This has led to the development of two additional SPM allocation algorithms. While differing in whether live range splits and spills are done sequentially or together, both algorithms place data aggregates in SPM based on the cliques in an interference graph. In both cases, we guarantee optimally that all data aggregates in an interference graph can be placed in SPM if the given SPM size is no smaller than the chromatic number of the graph. We have developed two memory colouring algorithms and two perfect colouring algorithms for SPM allocation. We have evaluated them using a set of embedded applications. Our results show that both methodologies are efficient and effective in handling large-scale embedded applications. While neither methodology outperforms the other consistently, perfect colouring has yielded better overall results in the set of benchmarks used in our experiments. All these algorithms are expected to be valuable. For example, they can be made available as part of the same compiler framework to assist the embedded designer with exploring a large number of optimisation opportunities for a particular embedded application.

Scratch-pad memory (SPM), a fast on-chip SRAM managed by software, is widely used in embedded systems. Compared to hardware-managed cache, SPM can be more efficient in performance, power and area cost, and has the added advantage of better time predictability. In this thesis, SPMs should be seen in a general context. For example, in stream processors, a software-managed stream register file is usually used to stage data to and from off-chip memory. In IBM's Cell architecture, each co-processor has a software-managed local store for keeping data and instructions. SPM management is critical for SPM-based embedded systems. In this thesis, we propose two novel methodologies, the memory colouring methodology and the perfect colouring methodology, to place the static data aggregates such as arrays and structs of a program in SPM. Our methodologies are dynamic in the sense that some data aggregates can be swapped into and out of SPM during program execution. To this end, a live range splitting heuristic is introduced in order to create potential data transfer statements between SPM and off-chip memory. The memory colouring methodology is a general-purpose compiler approach. The novelty of this approach lies in partitioning an SPM into a pseudo register file then generalising existing graph colouring algorithms for register allocation to colour data aggregates. In this thesis, a scheme for partitioning an SPM into a pseudo register file is introduced. This methodology is inter-procedural and therefore operates on the interference graph for the data aggregates in the whole program. Different graph colouring algorithms may give rise to different results due to live range splitting and spilling heuristics used. As a result, two representative graph colouring algorithms, George and Appel's iterative-coalescing and Park and Moon's optimistic-coalescing, are generalised and evaluated for SPM allocation. Like memory colouring, perfect colouring is also inter-procedural. The novelty of this second methodology lies in formulating the SPM allocation problem as an interval colouring problem. The interval colouring problem is an NP problem and no widely-accepted approximation algorithms exist. The key observation is that the interference graphs for data aggregates in many embedded applications form a special class of superperfect graphs. This has led to the development of two additional SPM allocation algorithms. While differing in whether live range splits and spills are done sequentially or together, both algorithms place data aggregates in SPM based on the cliques in an interference graph. In both cases, we guarantee optimally that all data aggregates in an interference graph can be placed in SPM if the given SPM size is no smaller than the chromatic number of the graph. We have developed two memory colouring algorithms and two perfect colouring algorithms for SPM allocation. We have evaluated them using a set of embedded applications. Our results show that both methodologies are efficient and effective in handling large-scale embedded applications. While neither methodology outperforms the other consistently, perfect colouring has yielded better overall results in the set of benchmarks used in our experiments. All these algorithms are expected to be valuable. For example, they can be made available as part of the same compiler framework to assist the embedded designer with exploring a large number of optimisation opportunities for a particular embedded application.

Scratch-pad memory (SPM), a fast on-chip SRAM managed by software, is widely used in embedded systems. Compared to hardware-managed cache, SPM can be more efficient in performance, power and area cost, and has the added advantage of better time predictability. In this thesis, SPMs should be seen in a general context. For example, in stream processors, a software-managed stream register file is usually used to stage data to and from off-chip memory. In IBM's Cell architecture, each co-processor has a software-managed local store for keeping data and instructions. SPM management is critical for SPM-based embedded systems. In this thesis, we propose two novel methodologies, the memory colouring methodology and the perfect colouring methodology, to place the static data aggregates such as arrays and structs of a program in SPM. Our methodologies are dynamic in the sense that some data aggregates can be swapped into and out of SPM during program execution. To this end, a live range splitting heuristic is introduced in order to create potential data transfer statements between SPM and off-chip memory. The memory colouring methodology is a general-purpose compiler approach. The novelty of this approach lies in partitioning an SPM into a pseudo register file then generalising existing graph colouring algorithms for register allocation to colour data aggregates. In this thesis, a scheme for partitioning an SPM into a pseudo register file is introduced. This methodology is inter-procedural and therefore operates on the interference graph for the data aggregates in the whole program. Different graph colouring algorithms may give rise to different results due to live range splitting and spilling heuristics used. As a result, two representative graph colouring algorithms, George and Appel's iterative-coalescing and Park and Moon's optimistic-coalescing, are generalised and evaluated for SPM allocation. Like memory colouring, perfect colouring is also inter-procedural. The novelty of this second methodology lies in formulating the SPM allocation problem as an interval colouring problem. The interval colouring problem is an NP problem and no widely-accepted approximation algorithms exist. The key observation is that the interference graphs for data aggregates in many embedded applications form a special class of superperfect graphs. This has led to the development of two additional SPM allocation algorithms. While differing in whether live range splits and spills are done sequentially or together, both algorithms place data aggregates in SPM based on the cliques in an interference graph. In both cases, we guarantee optimally that all data aggregates in an interference graph can be placed in SPM if the given SPM size is no smaller than the chromatic number of the graph. We have developed two memory colouring algorithms and two perfect colouring algorithms for SPM allocation. We have evaluated them using a set of embedded applications. Our results show that both methodologies are efficient and effective in handling large-scale embedded applications. While neither methodology outperforms the other consistently, perfect colouring has yielded better overall results in the set of benchmarks used in our experiments. All these algorithms are expected to be valuable. For example, they can be made available as part of the same compiler framework to assist the embedded designer with exploring a large number of optimisation opportunities for a particular embedded application.

Scratch-pad memory (SPM), a fast on-chip SRAM managed by software, is widely used in embedded systems. Compared to hardware-managed cache, SPM can be more efficient in performance, power and area cost, and has the added advantage of better time predictability. In this thesis, SPMs should be seen in a general context. For example, in stream processors, a software-managed stream register file is usually used to stage data to and from off-chip memory. In IBM's Cell architecture, each co-processor has a software-managed local store for keeping data and instructions. SPM management is critical for SPM-based embedded systems. In this thesis, we propose two novel methodologies, the memory colouring methodology and the perfect colouring methodology, to place the static data aggregates such as arrays and structs of a program in SPM. Our methodologies are dynamic in the sense that some data aggregates can be swapped into and out of SPM during program execution. To this end, a live range splitting heuristic is introduced in order to create potential data transfer statements between SPM and off-chip memory. The memory colouring methodology is a general-purpose compiler approach. The novelty of this approach lies in partitioning an SPM into a pseudo register file then generalising existing graph colouring algorithms for register allocation to colour data aggregates. In this thesis, a scheme for partitioning an SPM into a pseudo register file is introduced. This methodology is inter-procedural and therefore operates on the interference graph for the data aggregates in the whole program. Different graph colouring algorithms may give rise to different results due to live range splitting and spilling heuristics used. As a result, two representative graph colouring algorithms, George and Appel's iterative-coalescing and Park and Moon's optimistic-coalescing, are generalised and evaluated for SPM allocation. Like memory colouring, perfect colouring is also inter-procedural. The novelty of this second methodology lies in formulating the SPM allocation problem as an interval colouring problem. The interval colouring problem is an NP problem and no widely-accepted approximation algorithms exist. The key observation is that the interference graphs for data aggregates in many embedded applications form a special class of superperfect graphs. This has led to the development of two additional SPM allocation algorithms. While differing in whether live range splits and spills are done sequentially or together, both algorithms place data aggregates in SPM based on the cliques in an interference graph. In both cases, we guarantee optimally that all data aggregates in an interference graph can be placed in SPM if the given SPM size is no smaller than the chromatic number of the graph. We have developed two memory colouring algorithms and two perfect colouring algorithms for SPM allocation. We have evaluated them using a set of embedded applications. Our results show that both methodologies are efficient and effective in handling large-scale embedded applications. While neither methodology outperforms the other consistently, perfect colouring has yielded better overall results in the set of benchmarks used in our experiments. All these algorithms are expected to be valuable. For example, they can be made available as part of the same compiler framework to assist the embedded designer with exploring a large number of optimisation opportunities for a particular embedded application.

Techn. University, Diss., 1999--Berlin.

abstract: Thanks to continuous technology scaling, intelligent, fast and smaller digital systems are now available at affordable costs. As a result, digital systems have found use in a wide range of application areas that were not even imagined before, including medical (e.g., MRI, remote or post-operative monitoring devices, etc.), automotive (e.g., adaptive cruise control, anti-lock brakes, etc.), security systems (e.g., residential security gateways, surveillance devices, etc.), and in- and out-of-body sensing (e.g., capsule swallowed by patients measuring digestive system pH, heart monitors, etc.). Such computing systems, which are completely embedded within the application, are called embedded systems, as opposed to general purpose computing systems. In the design of such embedded systems, power consumption and reliability are indispensable system requirements. In battery operated portable devices, the battery is the single largest factor contributing to device cost, weight, recharging time, frequency and ultimately its usability. For example, in the Apple iPhone 4 smart-phone, the battery is $40\%$ of the device weight, occupies $36\%$ of its volume and allows only $7$ hours (over 3G) of talk time. As embedded systems find use in a range of sensitive applications, from bio-medical applications to safety and security systems, the reliability of the computations performed becomes a crucial factor. At our current technology-node, portable embedded systems are prone to expect failures due to soft errors at the rate of once-per-year; but with aggressive technology scaling, the rate is predicted to increase exponentially to once-per-hour. Over the years, researchers have been successful in developing techniques, implemented at different layers of the design-spectrum, to improve system power efficiency and reliability. Among the layers of design abstraction, I observe that the interface between the compiler and processor micro-architecture possesses a unique potential for efficient design optimizations. A compiler designer is able to observe and analyze the application software at a finer granularity; while the processor architect analyzes the system output (power, performance, etc.) for each executed instruction. At the compiler micro-architecture interface, if the system knowledge at the two design layers can be integrated, design optimizations at the two layers can be modified to efficiently utilize available resources and thereby achieve appreciable system-level benefits. To this effect, the thesis statement is that, ``by merging system design information at the compiler and micro-architecture design layers, smart compilers can be developed, that achieve reliable and power-efficient embedded computing through: i) Pure compiler techniques, ii) Hybrid compiler micro-architecture techniques, and iii) Compiler-aware architectures''. In this dissertation demonstrates, through contributions in each of the three compiler-based techniques, the effectiveness of smart compilers in achieving power-efficiency and reliability in embedded systems. / Dissertation/Thesis / Ph.D. Computer Science 2012

abstract: Integrated circuits must be energy efficient. This efficiency affects all aspects of chip design, from the battery life of embedded devices to thermal heating on high performance servers. As technology scaling slows, future generations of transistors will lack the energy efficiency gains as it has had in previous generations. Therefore, other sources of energy efficiency will be much more important. Many computations have the potential to be executed for extreme energy efficiency but are not instigated because the platforms they run on are not optimized for efficient execution. ASICs improve energy efficiency by reducing flexibility and leveraging the properties of a specific computation. However, ASICs are fixed in function and therefore have incredible opportunity cost. FPGAs offer a reconfigurable solution but are 25x less energy efficient than ASIC implementation. Spatially programmable architectures (SPAs) are similar in design and structure to ASICs and FPGAs but are able bridge the ASIC-FPGA energy efficiency gap by trading flexibility for efficiency. However, SPAs are difficult to program because they do not share the same programming model as normal architectures that execute in time. This work addresses compiler challenges for coarse grained, locally interconnected SPA for domain efficiency (SPADE). A novel SPADE topology, called the wave pipeline, is introduced that is designed for the image signal processing domain that is both efficient and simple to compile to. A compiler for the wave pipeline is created that solves for maximum energy and area efficiency using low complexity, greedy methods. The wave pipeline topology and compiler allow for us to investigate and experiment with image signal processing applications to prove the feasibility of SPADE compilers. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2016

abstract: Cyber-physical systems and hard real-time systems have strict timing constraints that specify deadlines until which tasks must finish their execution. Missing a deadline can cause unexpected outcome or endanger human lives in safety-critical applications, such as automotive or aeronautical systems. It is, therefore, of utmost importance to obtain and optimize a safe upper bound of each task’s execution time or the worst-case execution time (WCET), to guarantee the absence of any missed deadline. Unfortunately, conventional microarchitectural components, such as caches and branch predictors, are only optimized for average-case performance and often make WCET analysis complicated and pessimistic. Caches especially have a large impact on the worst-case performance due to expensive off- chip memory accesses involved in cache miss handling. In this regard, software-controlled scratchpad memories (SPMs) have become a promising alternative to caches. An SPM is a raw SRAM, controlled only by executing data movement instructions explicitly at runtime, and such explicit control facilitates static analyses to obtain safe and tight upper bounds of WCETs. SPM management techniques, used in compilers targeting an SPM-based processor, determine how to use a given SPM space by deciding where to insert data movement instructions and what operations to perform at those program locations. This dissertation presents several management techniques for program code and stack data, which aim to optimize the WCETs of a given program. The proposed code management techniques include optimal allocation algorithms and a polynomial-time heuristic for allocating functions to the SPM space, with or without the use of abstraction of SPM regions, and a heuristic for splitting functions into smaller partitions. The proposed stack data management technique, on the other hand, finds an optimal set of program locations to evict and restore stack frames to avoid stack overflows, when the call stack resides in a size-limited SPM. In the evaluation, the WCETs of various benchmarks including real-world automotive applications are statically calculated for SPMs and caches in several different memory configurations. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2017