Retargetable Compilers for Embedded DSPs

Programmable devices are a key technology for the design of embedded systems, such as in the consumer electronics market. Processor cores are used as building blocks for more and more embedded system designs, since they provide a unique combination of features: flexibility and reusability. Processor-based design implies that compilers capable of generating efficient machine code are necessary. However, highly efficient compilers for embedded processors are hardly available. In particular, this holds for digital signal processors (DSPs). This contribution is intended to outline different aspects of DSP compiler technology. First, we cover demands on compilers for embedded DSPs, which are partially in sharp contrast to traditional compiler construction. Secondly, we present recent advances in DSP code optimization techniques, which explore a comparatively large search space in order to achieve high code quality. Finally, we discuss the different approaches to retargetability of compilers, that is, techniques for automatic generation of compilers from processor models

Digital signal processor fundamentals and system design

Digital Signal Processors (DSPs) have been used in accelerator systems for more than fifteen years and have largely contributed to the evolution towards digital technology of many accelerator systems, such as machine protection, diagnostics and control of beams, power supply and motors. This paper aims at familiarising the reader with DSP fundamentals, namely DSP characteristics and processing development. Several DSP examples are given, in particular on Texas Instruments DSPs, as they are used in the DSP laboratory companion of the lectures this paper is based upon. The typical system design flow is described; common difficulties, problems and choices faced by DSP developers are outlined; and hints are given on the best solution

Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code

This paper introduces Tiramisu, a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines. Tiramisu introduces a scheduling language with novel extensions to explicitly manage the complexities that arise when targeting these systems. The framework is designed for the areas of image processing, stencils, linear algebra and deep learning. Tiramisu has two main features: it relies on a flexible representation based on the polyhedral model and it has a rich scheduling language allowing fine-grained control of optimizations. Tiramisu uses a four-level intermediate representation that allows full separation between the algorithms, loop transformations, data layouts, and communication. This separation simplifies targeting multiple hardware architectures with the same algorithm. We evaluate Tiramisu by writing a set of image processing, deep learning, and linear algebra benchmarks and compare them with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu matches or outperforms existing compilers and libraries on different hardware architectures, including multicore CPUs, GPUs, and distributed machines.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0041

Survey on Combinatorial Register Allocation and Instruction Scheduling

Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to increase instruction-level parallelism) are essential tasks for generating efficient assembly code in a compiler. In the last three decades, combinatorial optimization has emerged as an alternative to traditional, heuristic algorithms for these two tasks. Combinatorial optimization approaches can deliver optimal solutions according to a model, can precisely capture trade-offs between conflicting decisions, and are more flexible at the expense of increased compilation time. This paper provides an exhaustive literature review and a classification of combinatorial optimization approaches to register allocation and instruction scheduling, with a focus on the techniques that are most applied in this context: integer programming, constraint programming, partitioned Boolean quadratic programming, and enumeration. Researchers in compilers and combinatorial optimization can benefit from identifying developments, trends, and challenges in the area; compiler practitioners may discern opportunities and grasp the potential benefit of applying combinatorial optimization

Address optimizations for embedded processors

Embedded processors that are common in electronic devices perform a limited set of tasks compared to general-purpose processor systems. They have limited resources which have to be efficiently used. Optimal utilization of program memory needs a reduction in code size which can be achieved by eliminating unnecessary address computations i.e., generate optimal offset assignment that utilizes built-in addressing modes. Single offset assignment (SOA) solutions, used for processors with one address register; start with the access sequence of variables to determine the optimal assignment. This research uses the basic block to commutatively transform statements to alter the access sequence. Edges in the access graphs are classified into breakable and unbreakable edges. Unbreakable edges are preferred when selecting edges for the assignment. Breakable edges are used to commutatively transform statements such that the assignment cost is reduced. The use of a modify register in some processors allows the address to be modified by a value in MR in addition to post-increment/decrement modes. Though finding the most beneficial value of MR is a common practice, this research shows that modifying the access sequence using edge fold, node swap, and path interleave techniques for an MR value of two has significant benefit. General offset assignment requires variables in the access sequence to be partitioned to various address registers. Use of the node degree in the access graph demonstrates greater benefit than using edge weights and frequency of variables. The Static Single Assignment (SSA) form of the basic block introduces new variables to an access graph, making it sparser. Sparser access graphs usually have lower assignment costs. The SSA form allows reuse of variable space based on variable lifetimes. Offset assignment solutions may be improved by incrementally assignment based on uncovered edges, providing the best cost improvement. This heuristic considers improvements due to all uncovered edges. Optimization techniques have primarily been edge-based. Node-based SOA technique has been tested for use with commutative transformations and shown to be better than edge-based heuristics. Heuristics developed in this research perform address optimizations for embedded processors, employing new techniques that lower address computation costs

Heuristics for memory access optimization in embedded processors

Digital signal processors (DSPs) such as the Motorola 56k are equipped with two memory banks that are accessible in parallel in order to offer high memory bandwidth, which is required for high-performance applications. In order to make efficient use of the memory bandwidth offered by two or more memory banks, compilers for such DSPs should be capable of appropriately partitioning the program variables between the two memory banks and scheduling accesses. If two variables can be accessed simultaneously, then it is essential to have these two variables assigned to two different memory banks. Also if these two variables are in different banks, then instead of using two separate instructions for accessing the variables, both the accesses can be encoded into a single instruction, thereby reducing the code size as well. An efficient heuristic for maximizing the parallel accesses in DSPs with dual memory banks is proposed and evaluated. The heuristic is shown to be very effective on several examples. Architectures like the M3 DSP have a group memory for the single-instruction multiple-data (SIMD) architecture, for which addressing an element of the group means to access all the elements of that group in parallel, so there is no need for separately addressing each element of the group. Given a variable access sequence for a particular code, instead of separately accessing each one of the variables, if the variables are grouped then the number of memory accesses can be reduced as per SIMD paradigm. An efficient way of forming groups can significantly reduce the memory accesses. Two solutions for this problem are presented in this thesis. First, a novel integer linear programming formulation for forming the groups, thereby reducing the number of memory accesses in DSPs with SIMD architecture is presented. Second, a heuristic based on the solution for optimizing multiple memory bank accesses is presented and evaluated for this problem. Results on several graphs show the effectiveness of the heuristic

Neuroverkon inferenssi digitaalisessa signaalikäsittelyssä kovien reaaliaikavaatimusten alaisuudessa

The main objective of this thesis is to investigate how neural network inference can be efficiently implemented on a digital signal processor under hard real-time constraints from the execution speed point of view. Theories on digital signal processors and software optimization as well as neural networks are discussed. A neural network model for the specific use case is designed and a digital signal processor implementation is created based on the neural network model. A neural network model for the use case is created based on the data from the Matlab simulation model. The neural network model is trained and validated using the Python programming language with the Keras package. The neural network model is implemented on the CEVA-XC4500 digital signal processor. The digital signal processor implementation is written in C++ language with the processor specific vector-processing intrinsics. The neural network model is evaluated based on the model accuracy, precision, recall and f1-score. The model performance is compared to the conventional use case implementation by calculating 3GPP specified metrics of misdetection probability, false alarm rate and bit error rate. The execution speed of the digital signal processor implementation is evaluated with the CEVA integrated development environment profiling tool and also with the Lauterbach PowerTrace profiling module attached to the real base station product. Through this thesis, an optimized CEVA-XC4500 digital signal processor implementation was created for the specific neural network architecture. The optimized implementation showed to consume 88 percent less cycles than the conventional implementation. Also, the neural network model performance fulfills the 3GPP specification requirements.Tämän diplomityön tarkoituksena on tutkia miten neuroverkon inferenssi voidaan toteuttaa tehokkaasti digitaalisella signaaliprosessorilla suoritusnopeuden näkökulmasta, kun sovelluksella on kovat reaaliaikavaatimukset. Työssä käsitellään teoriaa digitaalisista signaaliprosessoreista, ohjelmistojen optimoinnista ja neuroverkoista. Työssä kehitetään neuroverkkomalli tiettyyn käyttötapaukseen, ja mallin pohjalta luodaan toteutus digitaaliselle signaaliprosessorille. Neuroverkkomalli luodaan Matlab-simulointimallin avulla kerätystä datasta. Neuroverkkomalli opetetaan ja varmennetaan Python-ohjelmointikiellellä ja Keras-paketilla. Neuroverkkomalli toteutetaan CEVA-XC4500 digitaaliselle signaaliprosessorille. Digitaalisen signaaliprosessorin toteutus kirjoitetaan C++-ohjelmointikielellä ja prosessorikohtaisilla vektorilaskentaoperaatioilla. Neuroverkkomalli varmennetaan mallin tarkkuuden, precision-arvon, recall-arvon ja f1-arvon perusteella. Mallin suorituskykyä verrataan käyttötapauksen tavanomaiseen toteutukseen laskemalla 3GPP-spesifikaation mukaiset mittarit virhehavaintotodennäköisyys, väärien hälytysten lukumäärä ja bittivirhemäärä. Suoritusnopeus määritetään sekä CEVA-ohjelmointiympäristön profilointityökalulla että tukiasematuotteeseen kytketyllä Lauterbach PowerTrace-yksiköllä. Työn tuloksena luotiin optimoitu CEVA-XC4500 digitaalinen signaaliprosessoritoteutus valitulle neuroverkkoarkkitehtuurille. Optimoitu toteutus kulutti 88% vähemmän laskentasyklejä kuin tavanomainen toteutus. Neuroverkkomalli täytti 3GPP-spesifikaation mukaiset vaatimukset

Alocação global de registradores de endereçamento para referencias a vetores em DSPs

Orientador: Guido Costa Souza de AraujoDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O avanço tecnológico dos sistemas computacionais tem proporcionado o crescimento do mercado de sistemas dedicados, cada vez mais comuns no dia-a-dia das pessoas, como por exemplo em telefones celulares, palmtops e sistemas de controle automotivo. Devido às suas características, estas novas aplicações requerem sistemas que aliem baixo custo, alto desempenho e baixo consumo de potência. Uma das maneiras de atender a estes requisitos é utilizando processadores especializados. Contudo, a especialização na arquitetura dos processadores impõe novos desafios para o desenvolvimento de software para estes sistemas. Em especial, os compiladores - geralmente responsáveis pela otimização de código - precisam ser adaptados para produzir código eficiente para estes novos processadores. Na área de processamento de sinais digitais, como em telefonia celular, processadores especializados, denominados DSPs2, são amplamente utilizados. Estes processadores tipicamente possuem poucos registradores de propósito geral e modos de endereçamento bastante limitados. Além disso, muitas das suas aplicações envolvem o processamento de grandes seqüências de dados, as quais são geralmente armazenadas em vetores. Como resultado, o estudo de técnicas de otimização de referências a vetores tornou-se um problema central em compilação para DSPs. Este problema, denominado Global Array Reference Allocation (GARA), é o objeto central desta dissertação. O sub-problema central de GARA consiste em se determinar, para um dado conjunto de referências a vetores que serão alocadas a um mesmo registrador de endereçamento, o menor custo das instruções que são necessárias para manter este registrador com o endereço adequado em cada ponto do programa. Nesta dissertação, este sub-problema é modelado como um problema em grafos, e provado ser NP-difícil. Além disso, é proposto um algoritmo eficiente, baseado em programação dinâmica, para resolver este sub-problema de forma exata sob certas restrições. Com base neste algoritmo, duas técnicas são propostas para resolver o problema de GARA. Resultados experimentais, obtidos pela implementação destas técnicas no compilador GCC, comparam-nas com outros resultados da literatura. Os resultados demonstram a eficácia das técnicas propostas nesta dissertaçãoAbstract: The technological advances in computing systems have stimulated the growth of the embedded systems market, which is continuously becoming more ordinary in people's lives, for example in mobile phones, palmtops and automotive control systems. Because of their characteristics, these new applications demand the combination of low cost, high performance and low power consumption. One way to meet these constraints is through the design of specialized processors. However, processor specialization imposes new challenges to the development of software for these systems. In particular, compilers - generally responsible for code optimization - need to be adapted in order to produce efficient code for these new processors. In the digital signal processing arena, such as in cellular telephones, specialized processors, known as DSPs (Digital Signal Processors), are largely used. DSPs typically have few general purpose registers and very restricted addressing modes. In addition, many DSP applications include large data streams processing, which are usually stored in arrays. As a result, studing array reference optimization techniques became an important task in compiling for DSPs. This work studies this problem, known as Global Array Reference Allocation (GARA). The central GARA subproblem consists of determining, for a given set of array references to be allocated to the same address register, the minimum cost of the instructions required to keep this register with the correct address at alI program points. In this work, this subproblem is modeled as a graph theoretical problem and proved to be NP-hard. In addition, an efficient algorithm, based on dynamic programming, is proposed to optimally solve this subproblem under some restrictions. Based on this algorithm, two techniques to solve GARA are proposed. Experimental results, from the implementation of these techniques in the GCC compiler, compare them with previous work in the literature. The results show the effectiveness of the techniques proposed in this workMestradoMestre em Ciência da Computaçã