Search results for "Design flow"
showing 5 items of 5 documents
Improving topological mapping on NoCs
2010
Networks-on-Chip (NoCs) have been proposed as an efficient solution to the complex communications on System-on-chip (SoCs). The design flow of network-on-chip (NoCs) include several key issues, and one of them is the decision of where cores have to be topologically mapped. This thesis proposes a new approach to the topological mapping strategy for NoCs. Concretely, we propose a new topological mapping technique for regular and irregular NoC platforms and its application for optimizing application specific NoC based on distributed and source routing.
Dual-model approach for safety-critical embedded systems
2020
Abstract The paper presents the design of digital controllers based on two models: the Petri net model, and the UML state machine. These two approaches differ in many aspects of design flow, such as conceptual modelling, and analysis and synthesis. Each of these approaches can be used individually to design an efficient logic controller, and such solutions are well-known, but their interoperability can contribute to a much better understanding of logic controller design and validation. This is especially important in the case of safety- or life-critical embedded systems, and apart from this, a dual-model controller design can make up redundant system increasing its reliability.
Learning automata based energy-efficient AI hardware design for IoT applications
2020
Energy efficiency continues to be the core design challenge for artificial intelligence (AI) hardware designers. In this paper, we propose a new AI hardware architecture targeting Internet of Things applications. The architecture is founded on the principle of learning automata, defined using propositional logic. The logic-based underpinning enables low-energy footprints as well as high learning accuracy during training and inference, which are crucial requirements for efficient AI with long operating life. We present the first insights into this new architecture in the form of a custom-designed integrated circuit for pervasive applications. Fundamental to this circuit is systematic encodin…
Modeling RISC-V Processor in IP-XACT
2018
IP-XACT is the most used standard in IP (Intellectual Property) integration. It is intended as a language neutral golden reference, from which RTL and HW dependent SW is automatically generated. Despite its wide popularity in the industry, there are practically no public and open design examples for any part of the design flow from IP-XACT to synthesis. One reason is the difficulty of creating IP-XACT models for existing RTL projects. In this paper, we address the issues by modeling the PULPino RISC-V microprocessor that is written in SystemVerilog (SV) and the project distributed over several repositories. We propose how to solve the mismatching concepts between SV project and IP-XACT, and…
Design environment for hardware generation of SLFF neural network topologies with ELM training capability
2015
Extreme Learning Machine (ELM) is a noniterative training method suited for Single Layer Feed Forward Neural Networks (SLFF-NN). Typically, a hardware neural network is trained before implementation in order to avoid additional on-chip occupation, delay and performance degradation. However, ELM provides fixed-time learning capability and simplifies the process of re-training a neural network once implemented in hardware. This is an important issue in many applications where input data are continuously changing and a new training process must be launched very often, providing self-adaptation. This work describes a general SLFF-NN design environment to assist in the definition of neural netwo…