Code transformations based on speculative SDC scheduling

Code motion and speculations are usually exploited in the High Level Synthesis of control dominated applications to improve the performances of the synthesized designs. Selecting the transformations to be applied is not a trivial task: their effects can indeed indirectly spread across the whole design, potentially worsening the quality of the results. In this paper we propose a code transformation flow, based on a new extension of the System of Difference Constraints (SDC) scheduling algorithm, which introduces a large number of transformations, whose profitability is guaranteed by SDC formulation. Experimental results show that the proposed technique in average reduces the execution time of control dominated applications by 37% with respect to a commercial tool without increasing the area usage

Performance modeling of embedded applications with zero architectural knowledge

Performance estimation is a key step in the development of an embedded system. Normally, the performance evaluation is performed using a simulator or a performance mathematical model of the target architecture. However, both these approaches are usually based on the knowledge of the architectural details of the target. In this paper we present a methodology for automatically building an analytical model to estimate the performance of an application on a generic processor without requiring any information about the processor architecture but the one provided by the GNU GCC Intermediate Representation. The proposed methodology exploits the linear regression technique based on an application analysis performed on the Register Transfer Level internal representation of the GNU GCC compiler. The benefits of working with this type of model and with this intermediate representation are three: we take into account most of the compiler optimizations, we implicitly consider some architectural characteristics of the target processor and we can easily estimate the performance of portions of the specification. We validate our approach by evaluating with cross-validation technique the accuracy and the generality of the performance models built for the ARM926EJ-S and the LEON3 processor

Combining Target-independent Analysis with Dynamic Profiling to Build the Performance Model of a DSP

Fast and accurate performance estimation is a key aspect of heterogeneous embedded systems design flow, since cycle-accurate simulators, when exist, are usually too slow to be used during design space exploration. Performance estimation techniques are usually based on combination of estimation of the single processing elements which compose the system. Architectural characteristics of Digital Signal Processors (DSP), such as the presence of Single Instruction Multiple Data operations or of special hardware units to control loop executions, introduce peculiar aspects in the performance estimation problem. In this paper we present a methodology to estimate the performance of a function on a given dataset on a DSP. Estimation is performed combining the host profiling data with the function GNU GCC GIMPLE representation. Starting from the results of this analysis, we build a performance model of a DSP by exploiting the Linear Regression Technique. Use of GIMPLE representation allows to take directly into account the target-independent optimizations performed by the DSP compiler. We validate our approach by building a performance model of the MagicV DSP and by testing the model on a set of significative benchmarks

How well does brand personality predict brand choice ?

The research proposes a methodology allowing both the construction of a brand personality scale and the test of the ability of the scale to predict brand choice. A brand personality scale is developed and tested via exploratory and confirmatory analyses. A brand personality structure composed of 12 facets is uncovered and allows clearly differentiating brands belonging to the same market. Predictive power of the scale is then tested using binary regression models.Brand personality, Human personality, Binary regression models, Positionnement, Brand management

Fine grain analysis of simulators accuracy for calibrating performance models

In embedded system design, the tuning and validation of a cycle accurate simulator is a difficult task. The designer has to assure that the estimation error of the simulator meets the design constraints on every application. If an application is not correctly estimated, the designer has to identify on which parts of the application the simulator introduces an estimation error and consequently fix the simulator. However, detecting which are the mispredicted parts of a very large application can be a difficult process which requires a lot of time. In this paper we propose a methodology which helps the designer to fast and automatically isolate the portions of the application mispredicted by a simulator. This is accomplished by recursively analyzing the application source code trace highlighting the mispredicted sections of source code. The results obtained applying the methodology to the TSIM simulator show how our methodology is able to fast analyze large applications isolating small portions of mispredicted code

Using Efficient Path Profiling to Optimize Memory Consumption of On-Chip Debugging for High-Level Synthesis

High-Level Synthesis (HLS) for FPGAs is attracting popularity and is increasingly used to handle complex systems with multiple integrated components. To increase performance and efficiency, HLS flows now adopt several advanced optimization techniques. Aggressive optimizations and system level integration can cause the introduction of bugs that are only observable on-chip. Debugging support for circuits generated with HLS is receiving a considerable attention. Among the data that can be collected on chip for debugging, one of the most important is the state of the Finite State Machines (FSM) controlling the components of the circuit. However, this usually requires a large amount of memory to trace the behavior during the execution. This work proposes an approach that takes advantage of the HLS information and of the structure of the FSM to compress control flow traces and to integrate optimized components for on-chip debugging. The generated checkers analyze the FSM execution on-fly, automatically notifying when a bug is detected, localizing it and providing data about its cause. The traces are compressed using a software profiling technique, called Efficient Path Profiling (EPP), adapted for the debugging of hardware accelerators generated with HLS. With this technique, the size of the memory used to store control flow traces can be reduced up to 2 orders of magnitude, compared to state-of-the-art

Enabling Automated Bug Detection for IP-based Designs using High-Level Synthesis

Modern System-on-Chip (SoC) architectures are increasingly composed of Intellectual Property (IP) blocks, usually designed and provided by different vendors. This burdens system designers with complex system-level integration and verification. In this paper, we propose an approach that leverages HLS techniques to automatically find bugs in designs composed of multiple IP blocks. Our method is particularly suitable for industrial adoption because it works without exposing sensitive information (e.g., the design specification or the component generation process). This advocates the definition and the adoption of an interoperable format for cross-vendor hardware bug detection

Trace-based automated logical debugging for high-level synthesis generated circuits

In this paper we present an approach for debugging hardware designs generated by High-Level Synthesis (HLS), relieving users from the burden of identifying the signals to trace and from the error-prone task of manually checking the traces. The necessary steps are performed after HLS, independently of it and without affecting the synthesized design. For this reason our methodology should be easily adaptable to any HLS tools. The proposed approach makes full use of HLS compile time informations. The executions of the simulated design and the original C program can be compared, checking if there are discrepancies between values of C variables and signals in the design. The detection is completely automated, that is, it does not need any input but the program itself and the user does not have to know anything about the overall compilation process. The design can be validated on a given set of test cases and the discrepancies are detected by the tool. Relationships between the original high-level source code and the generated HDL are kept by the compiler and shown to the user. The granularity of such discrepancy analysis is per-operation and it includes the temporary variables inserted by the compiler. As a consequence the design can be debugged as is, with no restrictions on optimizations available during HLS. We show how this methodology can be used to identify different kind of bugs: 1) introduced by the HLS tool used for the synthesis; 2) introduced using buggy libraries of hardware components for HLS; 3) undefined behavior bugs in the original high-level source code