Ising machines are hardware solvers that aim to find the absolute or approximate ground states of the Ising model. The Ising model is of fundamental computational interest because any problem in the complexity class NP can be formulated as an Ising problem with only polynomial overhead, and thus a scalable Ising machine that outperforms existing standard digital computers could have a huge impact for practical applications. We survey the status of various approaches to constructing Ising machines and explain their underlying operational principles. The types of Ising machines considered here include classical thermal annealers based on technologies such as spintronics, optics, memristors and digital hardware accelerators; dynamical systems solvers implemented with optics and electronics; and superconducting-circuit quantum annealers. We compare and contrast their performance using standard metrics such as the ground-state success probability and time-to-solution, give their scaling relations with problem size, and discuss their strengths and weaknesses. Minimizing the energy of the Ising model is a prototypical combinatorial optimization problem, ubiquitous in our increasingly automated world. This Review surveys Ising machines — special-purpose hardware solvers for this problem — and examines the various operating principles and compares their performance. 

/pdf/ising-machines-as-hardware-solvers-of-combinatorial-2ul6a5j8.pdf

Ising machines as hardware solvers of combinatorial optimization problems

Energy consumed for transferring data across the processor memory hierarchy constitutes a large fraction of total system energy consumption, and this fraction has steadily increased with technology scaling. In this paper, we propose near-DRAM acceleration (NDA) architectures, which process data using accelerators 3D-stacked on DRAM devices comprising off-chip main memory modules. NDA transfers most data through high-bandwidth and low-energy 3D interconnects between accelerators and DRAM devices instead of low-bandwidth and high-energy off-chip interconnects between a processor and DRAM devices, substantially reducing energy consumption and improving performance. Unlike previous near-memory processing architectures, NDA is built upon commodity DRAM devices; apart from inserting through-silicon vias (TSVs) to 3D-interconnect DRAM devices and accelerators, NDA requires minimal changes to the commodity DRAM device and standard memory module architectures. This allows NDA to be more easily adopted in both existing and emerging systems. Our experiments demonstrate that, on average, our NDA-based system consumes 46% (68%) lower (data transfer) energy at 1.67× higher performance than a system that integrates the same accelerator logic within the processor itself.

NDA: Near-DRAM acceleration architecture leveraging commodity DRAM devices and standard memory modules

Reconfigurable architectures can bring unique capabilities to computational tasks. They offer the performance and energy efficiency of hardware with the flexibility of software. In some domains, they are the only way to achieve the required, real-time performance without fabricating custom integrated circuits. Their functionality can be upgraded and repaired during their operational lifecycle and specialized to the particular instance of a task. We survey the field of reconfigurable computing, providing a guide to the body-of-knowledge accumulated in architecture, compute models, tools, run-time reconfiguration, and applications.

Reconfigurable Computing Architectures

As general-purpose processors have hit the power wall and chip fabrication cost escalates alarmingly, coarse-grained reconfigurable architectures (CGRAs) are attracting increasing interest from both academia and industry, because they offer the performance and energy efficiency of hardware with the flexibility of software. However, CGRAs are not yet mature in terms of programmability, productivity, and adaptability. This article reviews the architecture and design of CGRAs thoroughly for the purpose of exploiting their full potential. First, a novel multidimensional taxonomy is proposed. Second, major challenges and the corresponding state-of-the-art techniques are surveyed and analyzed. Finally, the future development is discussed.

https://dl.acm.org/doi/pdf/10.1145/3357375

A Survey of Coarse-Grained Reconfigurable Architecture and Design: Taxonomy, Challenges, and Applications

CGRAs are promising as accelerators due to their improved energy-efficiency compared to FPGAs. Existing CGRAs support reconfigurability for operations, but not communications because of the static neighbor-to-neighbor interconnect, leading to both performance loss and increased complexity of the compiler. In this paper, we introduce HyCUBE, a novel CGRA architecture with a reconfigurable interconnect providing single-cycle communications between distant FUs, resulting in a new formulation of the application mapping problem that leads to the design of an efficient compiler. HyCUBE achieves 1.5× and 3× better performance-per-watt compared to a CGRA with standard NoC and a CGRA with neighbor-to-neighbor connectivity, respectively.

HyCUBE: A CGRA with Reconfigurable Single-cycle Multi-hop Interconnect

The latest biomedical applications require low energy consumption, high performance, and wide energy-performance scalability to adapt to various working environments. In this study, we present ULP-SRP, an energy-efficient reconfigurable processor for biomedical applications. ULP-SRP uses a Coarse-Grained Reconfigurable Array (CGRA) for high-performance data processing with low energy consumption. We adopted a compact-size CGRA and modified it to support dynamically switchable three performance modes with fine-grained power gating in order to further optimize the energy consumption. The energy-performance scalability is also accomplished with multiple performance modes and a Unified Memory Architecture (UMA). Experimental results show that ULP-SRP achieved 59p energy reduction compared to previous works. A technique of dynamic CGRA mode changing gives 18.9p energy reduction. ULP-SRP is a good candidate for future mobile healthcare devices.

ULP-SRP: Ultra Low-Power Samsung Reconfigurable Processor for Biomedical Applications

Coarse Grained Reconfigurable Architectures (CGRAs) have played a key role in the area of domain specific processors due to their programmability and runtime reconfigurability. The Coarse Grained Array (CGA) structure enables target designs to achieve high performance, but it is easy to fall into over-design in term of area. Moreover, the network overhead between the function units (FUs) seriously degrades its clock speed. In this paper, we propose a high performance CGRA that facilitates design space exploration (DSE) to reduce these overheads. It employs a concept of building blocks, named mini cores, to mitigate overhead involved in DSE that aims to achieve high clock speed and small area in the target design. The proposed approach reduces the design time more than 100 times compared with previous design. Experimental results show that the implemented architecture reduces logic area by 14.38% and improves clock frequency by 59.34% without performance loss.

Design space exploration and implementation of a high performance and low area Coarse Grained Reconfigurable Processor

A processor and a memory management method are provided. The processor includes a processor core, a cache which transceives data to/from the processor core via a single port, and stores the data accessed by the processor core, and a Scratch Pad Memory (SPM) which transceives the data to/from the processor core via at least one of a plurality of multi ports.

Processor and memory control method

Ray tracing is a 3D rendering method for generating an image by simulating the path of light It can generate high quality images, but it requires great computing power Recent advances in ray tracing technology enable realtime ray tracing on modern desktop CPUs/GPUs But in the current mobile environment, it is difficult because of inadequate computing power, memory bandwidth, and flexibility in mobile GPUs In this paper, we present a mobile ray tracing system using Samsung Reconfigurable Processor (SRP) SRP architecture includes a tightly coupled very long instruction word (VLIW) engine and coarse-grained reconfigurable array (CGRA) The VLIW engine is designed for general-purpose computations, such as function invocation and branch selection, and the coarsegrained reconfigurable array is specialized for data-intensive part of the program and can be configured dynamically We proposed iterative batch-based ray tracing algorithm for SRP, and optimized memory bandwidth with local memory and data cache Our ray tracing system is implemented on a commercial FPGA-based prototyping system The experimental results show that our system is suitable for the mobile ray tracing

Real-time ray tracing on coarse-grained reconfigurable processor

A profiler which provides information to optimize an application specific architecture processor and a program for the processor is provided. The profiler includes: an architecture analyzer which analyzes an architecture description, and generates architecture analysis information, the architecture description describing an architecture of an application specific architecture processor which comprises a plurality of processing elements; a static analyzer which analyzes program static information that describes static information of a program, and generates static analysis information; a dynamic analyzer which analyzes program dynamic information that describes dynamic information of the program, and generates dynamic analysis information, the dynamic information of the program being generated by simulating the program; and a cross profiling analyzer which generates information for optimizing the application specific architecture processor to implement the program based on at least one of the architecture analysis information, the static analysis information, and the dynamic analysis information.

Profiler for optimizing processor architecture and application

Popular Tasks used by Researchers

How can I help with your research?

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

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

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

Anyone with the link can view this chat. You can switch it back to private anytime.

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.

Jeongwook Kim

Author Tools

Chat about Author