Over the past decade, system-on-chip (SoC) designs have evolved to address the ever increasing complexity of applications, fueled by the era of digital convergence. Improvements in process technology have effectively shrunk board-level components so they can be integrated on a single chip. New on-chip communication architectures have been designed to support all inter-component communication in a SoC design. These communication architecture fabrics have a critical impact on the power consumption, performance, cost and design cycle time of modern SoC designs. As application complexity strains the communication backbone of SoC designs, academic and industrial R&D efforts and dollars are increasingly focused on communication architecture design. 

This book is a comprehensive reference on concepts, research and trends in on-chip communication architecture design. It will provide readers with a comprehensive survey, not available elsewhere, of all current standards for on-chip communication architectures.

KEY FEATURES
* A definitive guide to on-chip communication architectures, explaining key concepts, surveying research efforts and predicting future trends
* Detailed analysis of all popular standards for on-chip communication architectures
* Comprehensive survey of all research on communication architectures, covering a wide range of topics relevant to this area, spanning the past several years, and up to date with the most current research efforts
* Future trends that with have a significant impact on research and design of communication architectures over the next several years

On-Chip Communication Architectures: System on Chip Interconnect

Early routability prediction helps designers and tools perform preventive measures so that design rule violations can be avoided in a proactive manner. However, it is a huge challenge to have a predictor that is both accurate and fast. In this work, we study how to leverage convolutional neural network to address this challenge. The proposed method, called RouteNet, can either evaluate the overall routability of cell placement solutions without global routing or predict the locations of DRC (Design Rule Checking) hotspots. In both cases, large macros in mixed-size designs are taken into consideration. Experiments on benchmark circuits show that RouteNet can forecast overall routability with accuracy similar to that of global router while using substantially less runtime. For DRC hotspot prediction, RouteNet improves accuracy by 50% compared to global routing. It also significantly outperforms other machine learning approaches such as support vector machine and logistic regression.

https://dl.acm.org/doi/pdf/10.1145/3240765.3240843

RouteNet: routability prediction for mixed-size designs using convolutional neural network

The Nesterov’s method approach to analytic placement has recently demonstrated strong solution quality and scalability. We dissect the previous implementation strategy and show that solution quality can be significantly improved using two levers: 1) constraint-oriented local smoothing and 2) dynamic step size adaptation. We propose a new density function that comprehends local overflow of area resources; this enables a constraint-oriented local smoothing at per-bin granularity. Our improved dynamic step size adaptation automatically determines step size and effectively allocates optimization effort to significantly improve solution quality without undue runtime impact. Our resulting global placement tool, RePlAce, achieves an average of 2.00% half-perimeter wirelength (HPWL) reduction over all best known ISPD-2005 and ISPD-2006 benchmark results, and an average of 2.73% over all best known modern mixed-size (MMS) benchmark results, without any benchmark-specific code or tuning. We further extend our global placer to address routability, and achieve on average 8.50%–9.59% scaled HPWL reduction over previous leading academic placers for the DAC-2012 and ICCAD-2012 benchmark suites. To our knowledge, RePlAce is the first work to achieve superior solution quality across all the ISPD-2005, ISPD-2006, MMS, DAC-2012, and ICCAD-2012 benchmark suites with a single global placement engine.

RePlAce: Advancing Solution Quality and Routability Validation in Global Placement

Without the MCNC and ISPD98 benchmarks, it would arguably not have been possible for the academic community to make consistent advances in physical design over the last decade. While still being used extensively in placement and floorplanning research, those benchmarks can no longer be considered representative of today's (and tomorrow's) physical design challenges. In order to drive physical design research over the next few years, a new benchmark suit is being released in conjunction with the ISPD2005 placement contest. These benchmarks are directly derived from industrial ASIC designs, with circuit sizes ranging from 210 thousand to 2.1 million placeable objects. Unlike the ISPD98 benchmarks, the physical structure of these designs is completely preserved, giving realistic challenging designs for today's placement tools. Hopefully, these benchmarks will help accelerate new physical design research in the placement, floor-planning, and routing.

The ISPD2005 placement contest and benchmark suite

In this paper, we describe a new via-configurable routing architecture which shows a much better throughput and performance than the previous structures. We demonstrate how to construct a single-via-mask fabric to reduce the mask cost further, and we analyze the penalties which it incurs. To solve the routability problem commonly existing in fabric-based designs, an efficient white-space allocation and an incremental cell movement scheme are suggested, which help to provide a fast design convergence and early prediction of circuit's mappability to a given fabric

Via-Configurable Routing Architectures and Fast Design Mappability Estimation for Regular Fabrics

Congestion is a fundamental problem in VLSI design flows. Typically, it is handled by feeding back density information to the placers and routers. Fast and accurate congestion estimation is key in order to obtain a design flow with less iterations and higher predictability.Fast congestion prediction is based on an accurate approximation of the actual routing engine. In this paper we show experimentally that the number of two-pin nets with more than two bends in the actual router is negligible. It is also established that the ratio between the number of L-shapes and Z-shapes is more or less a constant.A fast and accurate algorithm for congestion prediction is developed. The above observations are translated into probabilities, that are used to "smear" out a net over its possible realizations. Extensive experimental evidence is provided using industrial designs.

Probabilistic congestion prediction

Fast congestion prediction is essential for congestion reduction techniques at different stages of the flow. Probabilistic congestion estimation methods model congestion after placement by considering the probability a wire will be routed over different areas of the routing region. They are popular because they are much faster than traditional global routing.In this paper, two congestion estimation tools are presented. The first one is an implementation of a probabilistic method called pce and appears to be very fast in comparison with comparable methods. The second one called FaDGloR is new and based on global routing techniques. Surprisingly, FaDGloR is about as fast as pce. The reason is that it is tuned towards congestion estimation and speed contrary to global routers that are tuned towards wire length reduction and routing as many wires as possible. Special focus is on congested areas of the chip, the areas that may prevent a design to be routable, and FaDGloR more accurately predicts these areas than pce. Both tools are tested on designs varying from impossible to route to easily routable. Previous papers focussed on accurately modeling congestion of the final, routable design. In practice, also unroutable designs need to be evaluated. The results presented in this paper indicate that global routing based methods are probably more worthwhile than probabilistic methods.

Is probabilistic congestion estimation worthwhile

Growing complexity of multiprocessor systems on chip (MP-SoC) requires future communication resources that can only be met by highly scalable architectures. Networks-on-Chip (NoCs) offer this scalability and other advantages like modularity, quality-of-service (QoS), possibly smaller area footprint and lower power dissipation. Although many papers describe the advantages of NoCs and describe techniques to apply NoCs on certain application domains, few have actually employed the complete design chain to make a netlist level implementation and area comparison (Steenhof et al., 2006) and (Angiolini et al., 2006). This paper describes the application of the AEligthereal NoC to an existing bus-based MP-SoC design and an area comparison with the original interconnects structure down to netlist level

/pdf/comparison-of-an-aethereal-network-on-chip-and-a-traditional-gkjndvxgb7.pdf

Comparison of An Æthereal Network on Chip and A Traditional Interconnect for A Multi-Processor DVB-T System on Chip

The growing complexity of multiprocessor systems on chip make the integration of Intellectual Property (IP) blocks into a working system a major challenge. Networks-on-Chip (NoCs) facilitate a modular design approach which addresses the hardware challenges in designing such a system. Guaranteed communication services, offered by the AEthereal NoC, address the software challenges by making the system more robust and easier to design. This paper describes two existing bus-based reference designs and compares the original interconnects with an AEthereal NoC. We show through these two case study implementations that the area cost of the NoC, which is dominated by the number of network connections, is competitive with traditional interconnects. Furthermore, we show that the latency in the NoC-based design is still acceptable for our application.

/pdf/comparison-of-an-aethereal-network-on-chip-and-traditional-u5mnpiijm1.pdf

Comparison of an Æthereal Network on Chip and Traditional Interconnects - Two Case Studies

As chip design becomes ever more complex, fewer design teams are succeeding. Who's to blame? On one hand, tools are hard to use, buggy, not interoperable, and have missing functionality. On the other hand, there is a wide range of engineering skills within the user population, and tools can be abused within flawed methodologies. This panel will quantify and prioritize the key gaps, including interoperability, that must be addressed on both sides.

Tools or users: which is the bigger bottleneck?

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Patrick Groeneveld

Author Tools

Chat about Author