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Proceedings ArticleDOI

A multi parametric optimization based novel approach for an efficient design space exploration for ASIC design

TLDR
This paper presents a novel approach to achieve a Pareto optimal solution for this design space exploration in minimum possible design time using Greedy Algorithm and Priority Factor for power and timing analysis.
Abstract
High level synthesis (HLS) is the methodology of generating Register Transfer Logic(RTL) design taking into consideration the behavioural specification and constraints within an optimized cost function. Design space exploration (DSE), an important stage of HLS, is a task for identifying and evaluating design alternatives during system development for obtaining Pareto optimal solution. Concerns over the power dissipation coupled with the conventional metrics such as area, time delay, thermal, performance, reliability, cost and testability have raised the demand for an efficient technique of high level synthesis with better design space exploration. This paper presents a novel approach to achieve a Pareto optimal solution for this design space exploration in minimum possible design time using Greedy Algorithm and Priority Factor (PF) for power and timing analysis.

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Citations
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Journal ArticleDOI

Development of Multiobjective High-Level Synthesis for FPGAs

TL;DR: A comprehensive analysis of different MOAs that are suitable to perform HLS for FPGA devices and highlights significant aspects of MOAs, namely, optimization methods, intermediate structures where the optimizations are performed, HLS techniques that are addressed, and benchmarks and performance assessments employed for experimentation.
Proceedings ArticleDOI

FPGA-based Design and Optimization of a 5G-NR DU Receiver

TL;DR: In this paper, a 5G-NR DU receiver is modelled using a fast implementation flow, from the behavioral model to the Field-Programmable Gate Array (FPGA) validation.
References
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Journal ArticleDOI

A genetic algorithm for the design space exploration of datapaths during high-level synthesis

TL;DR: This work presents a framework for efficient design space exploration during high-level synthesis of datapaths for data-dominated applications using a genetic algorithm to concurrently perform scheduling and allocation with the aim of finding schedules and module combinations that lead to superior designs while considering user-specified latency and area constraints.
Journal ArticleDOI

Efficient design space exploration for application specific systems-on-a-chip

TL;DR: This paper proposes an approach which tackles the problem of design space exploration (DSE) in both of the fronts of the reduction of the number of system configurations to be simulated and the reduced of the time required to evaluate a system configuration.
Journal ArticleDOI

GABIND: a GA approach to allocation and binding for the high-level synthesis of data paths

TL;DR: A technique for allocation and binding for data path synthesis (DPS) using a Genetic Algorithm approach relying on a force directed data path binding completion algorithm is presented.
Proceedings ArticleDOI

A Multi-objective Genetic Algorithm for Design Space Exploration in High-Level Synthesis

TL;DR: This paper will show that this approach is able to obtain better optimization results, with respect to the design objectives, in most of situations and the proposed encoding better approaches the situations when multi-modal functional units could be used in the final design solutions.
Proceedings ArticleDOI

Accelerating design space exploration using pareto-front arithmetics

TL;DR: This paper proposes Pareto-Front Arithmetics (PFA), an approach for the synthesis of heterogeneous (embedded) systems, while exploiting a hierarchical problem structure using results of subsystems to construct implementations of the top-level system.
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