Chippe : a system for constraint driven behavioral synthesis

This report describes the Chippe system, gives some background previous work and describes several sample design runs of the system. Also presented are the sources of the design tradeoffs used by Chippe, and overview of the internal design model, and experiences using the system

Mapping Applications to an FPFA Tile

This paper introduces a transformational design method which can be used to map code written in a high level source language, like C, to a coarse grain reconfigurable architecture. The source code is first translated into a control data flow graph (CDFG), which is minimized using a set of behaviour preserving transformations, such as dependency analysis, common subexpression elimination, etc. After applying graph clustering, scheduling and allocation transformations on this minimized graph, it can be mapped onto the target architecture

VSS : a VHDL synthesis system

This report describes a register transfer synthesis system that allows a designer to interact with the design process. The designer can modify the compiled design by changing the input description, selecting optimization and mapping strategies, or graphically changing the generated design schematic. The VHDL language is used for input and output descriptions. An intermediate representation which incorporates signal typing and component attributes simplifies compilation and facilitates design optimization. The compilation process consists of two phases. First, a design composed of generic components is synthesized from the input description. Second, this design is translated into components from a particular library by a mapper and optimized by a logic optimizer. Redesign to new technologies can be accomplished by changing only the component library

Optimizing construction of scheduled data flow graph for on-line testability

The objective of this work is to develop a new methodology for behavioural synthesis using a flow of synthesis, better suited to the scheduling of independent calculations and non-concurrent online testing. The traditional behavioural synthesis process can be defined as the compilation of an algorithmic specification into an architecture composed of a data path and a controller. This stream of synthesis generally involves scheduling, resource allocation, generation of the data path and controller synthesis. Experiments showed that optimization started at the high level synthesis improves the performance of the result, yet the current tools do not offer synthesis optimizations that from the RTL level. This justifies the development of an optimization methodology which takes effect from the behavioural specification and accompanying the synthesis process in its various stages. In this paper we propose the use of algebraic properties (commutativity, associativity and distributivity) to transform readable mathematical formulas of algorithmic specifications into mathematical formulas evaluated efficiently. This will effectively reduce the execution time of scheduling calculations and increase the possibilities of testability

Silicon compilation

Silicon compilation is a term used for many different purposes. In this paper we define silicon compilation as a mapping from some higher level description into layout. We define the basic issues in structural and behavioral silicon compilation and some possible solutions to those issues. Finally, we define the concept of an intelligent silicon compiler in which the compiler evaluates the quality of the generated design and attempts to improve it if it is not satisfactory

A General Large Neighborhood Search Framework for Solving Integer Programs

This paper studies how to design abstractions of large-scale combinatorial optimization problems that can leverage existing state-of-the-art solvers in general purpose ways, and that are amenable to data-driven design. The goal is to arrive at new approaches that can reliably outperform existing solvers in wall-clock time. We focus on solving integer programs, and ground our approach in the large neighborhood search (LNS) paradigm, which iteratively chooses a subset of variables to optimize while leaving the remainder fixed. The appeal of LNS is that it can easily use any existing solver as a subroutine, and thus can inherit the benefits of carefully engineered heuristic approaches and their software implementations. We also show that one can learn a good neighborhood selector from training data. Through an extensive empirical validation, we demonstrate that our LNS framework can significantly outperform, in wall-clock time, compared to state-of-the-art commercial solvers such as Gurobi