Memory-Based High-Level Synthesis Optimizations Security Exploration on the Power Side-Channel

High-level synthesis (HLS) allows hardware designers to think algorithmically and not worry about low-level, cycle-by-cycle details. This provides the ability to quickly explore the architectural design space and tradeoffs between resource utilization and performance. Unfortunately, security evaluation is not a standard part of the HLS design flow. In this article, we aim to understand the effects of memory-based HLS optimizations on power side-channel leakage. We use Xilinx Vivado HLS to develop different cryptographic cores, implement them on a Spartan-6 FPGA, and collect power traces. We evaluate the designs with respect to resource utilization, performance, and information leakage through power consumption. We have two important observations and contributions. First, the choice of resource optimization directive results in different levels of side-channel vulnerabilities. Second, the partitioning optimization directive can greatly compromise the hardware cryptographic system through power side-channel leakage due to the deployment of memory control logic. We describe an evaluation procedure for power side-channel leakage and use it to make best-effort recommendations about how to design more secure architectures in the cryptographic domain

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201

Rapid Industrial Prototyping and SoC Design of 3G/4G Wireless Systems Using an HLS Methodology

Many very-high-complexity signal processing algorithms are required in future wireless systems, giving tremendous challenges to real-time implementations. In this paper, we present our industrial rapid prototyping experiences on 3G/4G wireless systems using advanced signal processing algorithms in MIMO-CDMA and MIMO-OFDM systems. Core system design issues are studied and advanced receiver algorithms suitable for implementation are proposed for synchronization, MIMO equalization, and detection. We then present VLSI-oriented complexity reduction schemes and demonstrate how to interact these high-complexity algorithms with an HLS-based methodology for extensive design space exploration. This is achieved by abstracting the main effort from hardware iterations to the algorithmic C/C++ fixed-point design. We also analyze the advantages and limitations of the methodology. Our industrial design experience demonstrates that it is possible to enable an extensive architectural analysis in a short-time frame using HLS methodology, which significantly shortens the time to market for wireless systems.National Science Foundatio

Type-driven automated program transformations and cost modelling for optimising streaming programs on FPGAs

In this paper we present a novel approach to program optimisation based on compiler-based type-driven program transformations and a fast and accurate cost/performance model for the target architecture. We target streaming programs for the problem domain of scientific computing, such as numerical weather prediction. We present our theoretical framework for type-driven program transformation, our target high-level language and intermediate representation languages and the cost model and demonstrate the effectiveness of our approach by comparison with a commercial toolchain

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

In recent years, the field of Deep Learning has seen many disruptive and impactful advancements. Given the increasing complexity of deep neural networks, the need for efficient hardware accelerators has become more and more pressing to design heterogeneous HPC platforms. The design of Deep Learning accelerators requires a multidisciplinary approach, combining expertise from several areas, spanning from computer architecture to approximate computing, computational models, and machine learning algorithms. Several methodologies and tools have been proposed to design accelerators for Deep Learning, including hardware-software co-design approaches, high-level synthesis methods, specific customized compilers, and methodologies for design space exploration, modeling, and simulation. These methodologies aim to maximize the exploitable parallelism and minimize data movement to achieve high performance and energy efficiency. This survey provides a holistic review of the most influential design methodologies and EDA tools proposed in recent years to implement Deep Learning accelerators, offering the reader a wide perspective in this rapidly evolving field. In particular, this work complements the previous survey proposed by the same authors in [203], which focuses on Deep Learning hardware accelerators for heterogeneous HPC platforms

Rapid prototyping from algorithm to FPGA prototype

Abstract. Wireless data usage continuously increases in today’s world setting higher requirements for wireless networks. Ever increasing requirements result in more complex hardware (HW) implementation, especially telecommunication System-on-Chips (SoC) performance is playing a key-role in this development. Complexity increases design workload, therefore, it makes design flow times longer. High-Level Synthesis (HLS) tools have been designed to automate and accelerate design by moving manual work on a higher level. This Master’s Thesis studies MathWorks HLS workflow usage for rapid prototyping of Wireless Communication SoC Intellectual Property (IP). This thesis introduces design and FPGA prototyping flow of Application-Specific Integrated Circuit (ASIC). It presents good design practices targeted for HLS. It also studies MathWorks Hardware Description Language (HDL) generation flow with HDL Coder, possible problems during the flow and solutions to overcome the problems. The HLS flow is examined with an example design that scales and limits the power of IQ-data. This work verifies the design in a Field-Programmable Gate Array (FPGA) environment. It concentrates on evaluating the usage and benefits of MathWorks HLS workflow targeted for rapid prototyping of SoCs. The Example IP is a Simulink model containing MATLAB algorithms and System Objects. The design is optimized on algorithm level and synthesized into VHDL. The generated Register-Transfer Level (RTL) is verified in co-simulation against the algorithm model. Optimization and verification methods are evaluated. The HDL model is further processed through logic-synthesis using the 3rd party synthesis tool run automatically with a script created by MathWorks workflow. The generated design is tested on FPGA with FPGA-in-the-loop simulation configuration. FPGA prototyping flow benefits for rapid prototyping are evaluated. Coding styles to generate synthesizable HDL code and simulation methods to improve simulation speed of hardware-like algorithm were discussed. MathWorks HLS workflow was evaluated for rapid prototype purposes from algorithm to FPGA. Optimization methods and capability for production quality RTL for ASIC target were also discussed. MathWorks’ tool flow provided promising results for rapid prototyping. It generated human-readable HDL that was successfully synthesized on FPGA. The FPGA model was simulated in FPGA-in-the-loop configuration successfully. It also provided good area and speed results for the ASIC target when the algorithm was written strictly from the hardware perspective. The process was found to be distinct and efficient.Nopea prototypointi algoritmista FPGA-prototyypiksi. Tiivistelmä. Langattoman datan käyttö kasvaa jatkuvasti nykymaailmassa ja asettaa korkeammat vaatimukset langattomille verkoille. Kasvavat vaatimukset tekevät laitteistototeutuksesta kompleksisempaa, erityisesti tietoliikenteessä käytettävien järjestelmäpiirien (SoC) tehokkuus on avainasemassa. Tämä kasvattaa suunnittelun työmäärää ja näin ollen suunnitteluvuohon kuluva aika pidentyy. Korkean tason synteesi (HLS) on kehitetty automatisoimaan ja nopeuttamaan digitaalisuunnittelua siirtämällä manuaalista työtä korkeammalle tasolle. Tämä diplomityö tutkii MathWorks:n HLS-vuon käyttöä langattomaan viestintään suunniteltavien SoC:ien tekijänoikeudenalaisten standardoitujen lohkojen (IP) nopeaan prototypointiin. Työ esittelee perinteisen asiakaspiirin (ASIC) suunnitteluvuon, FPGA-prototypointivuon ja suunnitteluperiaatteet HLS:ää varten. Työssä käydään läpi MathWorks:n laitteistokuvauskielen (HDL) generointivuo HDL Coder:lla, mahdollisia ongelmakohtia vuossa ja ratkaisuja ongelmiin. HLS-vuota tutkitaan esimerkkimallin avulla, joka skaalaa ja rajoittaa IQ-datan tehoa. Esimerkkimallin toiminta tarkistetaan ohjelmoitavan logiikkapiirin (FPGA) kanssa. Työ keskittyy arvioimaan MathWorks:n HLS-vuon käyttöä ja hyötyä nopeaan prototypointiin SoC:ien kehityksessä. Esimerkkinä käytetään Simulink-mallia, joka sisältää MATLAB-funktioita ja System Object-olioita. Algoritmitasolla optimoitu malli syntesoidaan VHDL:ksi ja rekisterinsiirtotason (RTL) mallin toiminta tarkistetaan yhteissimulaatiolla alkuperäistä algoritmimallia vasten. Optimointi- ja verifiointimenetelmien toimivuutta ja tehokkuutta arvioidaan. Generoitu HDL-malli syntesoidaan kolmannen osapuolen logiikkasynteesi-työkalulla, joka käynnistetään MathWorks:n työkaluvuon generoimalla komentosarjalla. Luotu malli ohjelmoidaan FPGA:lle ja sen toiminta tarkistetaan FPGA-simulaatiolla. Syntesoituvan HDL-koodin generointiin vaadittavia koodaustyylejä ja algoritmimallin simulointinopeutta parantavia menetelmiä tutkittiin. MathWorks:n HLS-vuon soveltuvuutta nopeaan prototypointiin algoritmista FPGA-prototyypiksi pohdittiin. Lisäksi optimointimenetelmiä ja vuon soveltuvuutta tuotantolaatuisen RTL:n generoimiseen arvioitiin. MathWorks:n työkaluvuo osoitti lupaavia tuloksia nopean prototypoinnin näkökulmasta. Se loi luettavaa HDL-koodia, joka syntesoitui FPGA:lle. Malli ajettiin onnistuneesti FPGA:lla. Vuon avulla saavutettiin hyviä tuloksia pinta-alan ja nopeuden suhteen, kun malli optimoitiin asiakaspiirille. Tämä vaati mallin kuvaamista tarkasti laitteiston näkökulmasta. Prosessi oli kokonaisuudessaan selkeä ja tehokas

Hardware-software codesign in a high-level synthesis environment

Interfacing hardware-oriented high-level synthesis to software development is a computationally hard problem for which no general solution exists. Under special conditions, the hardware-software codesign (system-level synthesis) problem may be analyzed with traditional tools and efficient heuristics. This dissertation introduces a new alternative to the currently used heuristic methods. The new approach combines the results of top-down hardware development with existing basic hardware units (bottom-up libraries) and compiler generation tools. The optimization goal is to maximize operating frequency or minimize cost with reasonable tradeoffs in other properties. The dissertation research provides a unified approach to hardware-software codesign. The improvements over previously existing design methodologies are presented in the frame-work of an academic CAD environment (PIPE). This CAD environment implements a sufficient subset of functions of commercial microelectronics CAD packages. The results may be generalized for other general-purpose algorithms or environments. Reference benchmarks are used to validate the new approach. Most of the well-known benchmarks are based on discrete-time numerical simulations, digital filtering applications, and cryptography (an emerging field in benchmarking). As there is a need for high-performance applications, an additional requirement for this dissertation is to investigate pipelined hardware-software systems\u27 performance and design methods. The results demonstrate that the quality of existing heuristics does not change in the enhanced, hardware-software environment