A High Level Synthesis Flow Using Model Driven Engineering

Intensive Signal Processing (ISP) applications handle large amounts of data and are characterized by hierarchical and data parallel tasks, which manip- ulate multidimensional data arrays according to complex data dependencies. Performance requirements often preclude ISP applications from being im- plemented purely in software and instead call for using custom and efficient hardware accelerators. A hardware accelerator is an electronic design dedi- cated to the execution of a specific application. Its hardware architecture can be designed for a maximal parallelization of the algorithm needed to execute its application and for optimal execution support for regular and repetitive tasks. However, the complexity of hardware accelerators makes them difficult to manipulate at low abstraction levels (in a Hardware Description Language (HDL) for instance). The description of complex ISP applications is also error prone and tedious when using tools that constrain the number of dimensions of data arrays. High Level Synthesis (HLS) seeks to simplify the design of hardware accel- erators by describing applications at a high abstraction level and by generat- ing the corresponding low level implementation. Application specification is easier at a high abstraction level since hardware designers do not need to han- dle all low level implementation details. HLS thus aims to achieve algorithm- architecture matching by construction, through the automated synthesis of a hardware architecture for an application specified at a high level. The automatic generation of low level implementations drastically reduces non- recurring engineering costs and the time to market compared to hand-tuned implementations in HDL. For these reasons, HLS tools have been increasingly successful among the hardware designer community. This trend is followed by the continual integration of new capabilities and functionality in the tools. Therefore, successful HLS has to support rapidly evolving technologies and be maintainable in order to capitalize on efforts. We present some design challenges faced by HLS and how model-driven engineering can meet them

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

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

Automated Synthesis of SEU Tolerant Architectures from OO Descriptions

SEU faults are a well-known problem in aerospace environment but recently their relevance grew up also at ground level in commodity applications coupled, in this frame, with strong economic constraints in terms of costs reduction. On the other hand, latest hardware description languages and synthesis tools allow reducing the boundary between software and hardware domains making the high-level descriptions of hardware components very similar to software programs. Moving from these considerations, the present paper analyses the possibility of reusing Software Implemented Hardware Fault Tolerance (SIHFT) techniques, typically exploited in micro-processor based systems, to design SEU tolerant architectures. The main characteristics of SIHFT techniques have been examined as well as how they have to be modified to be compatible with the synthesis flow. A complete environment is provided to automate the design instrumentation using the proposed techniques, and to perform fault injection experiments both at behavioural and gate level. Preliminary results presented in this paper show the effectiveness of the approach in terms of reliability improvement and reduced design effort

3D Face Synthesis Driven by Personality Impression

Synthesizing 3D faces that give certain personality impressions is commonly needed in computer games, animations, and virtual world applications for producing realistic virtual characters. In this paper, we propose a novel approach to synthesize 3D faces based on personality impression for creating virtual characters. Our approach consists of two major steps. In the first step, we train classifiers using deep convolutional neural networks on a dataset of images with personality impression annotations, which are capable of predicting the personality impression of a face. In the second step, given a 3D face and a desired personality impression type as user inputs, our approach optimizes the facial details against the trained classifiers, so as to synthesize a face which gives the desired personality impression. We demonstrate our approach for synthesizing 3D faces giving desired personality impressions on a variety of 3D face models. Perceptual studies show that the perceived personality impressions of the synthesized faces agree with the target personality impressions specified for synthesizing the faces. Please refer to the supplementary materials for all results.Comment: 8pages;6 figure