ToPoliNano: Nanoarchitectures Design Made Real

Many facts about emerging nanotechnologies are yet to be assessed. There are still major concerns, for instance, about maximum achievable device density, or about which architecture is best fit for a specific application. Growing complexity requires taking into account many aspects of technology, application and architecture at the same time. Researchers face problems that are not new per se, but are now subject to very different constraints, that need to be captured by design tools. Among the emerging nanotechnologies, two-dimensional nanowire based arrays represent promising nanostructures, especially for massively parallel computing architectures. Few attempts have been done, aimed at giving the possibility to explore architectural solutions, deriving information from extensive and reliable nanoarray characterization. Moreover, in the nanotechnology arena there is still not a clear winner, so it is important to be able to target different technologies, not to miss the next big thing. We present a tool, ToPoliNano, that enables such a multi-technological characterization in terms of logic behavior, power and timing performance, area and layout constraints, on the basis of specific technological and topological descriptions. This tool can aid the design process, beside providing a comprehensive simulation framework for DC and timing simulations, and detailed power analysis. Design and simulation results will be shown for nanoarray-based circuits. ToPoliNano is the first real design tool that tackles the top down design of a circuit based on emerging technologie

Logic synthesis and testing techniques for switching nano-crossbar arrays

Beyond CMOS, new technologies are emerging to extend electronic systems with features unavailable to silicon-based devices. Emerging technologies provide new logic and interconnection structures for computation, storage and communication that may require new design paradigms, and therefore trigger the development of a new generation of design automation tools. In the last decade, several emerging technologies have been proposed and the time has come for studying new ad-hoc techniques and tools for logic synthesis, physical design and testing. The main goal of this project is developing a complete synthesis and optimization methodology for switching nano-crossbar arrays that leads to the design and construction of an emerging nanocomputer. New models for diode, FET, and four-terminal switch based nanoarrays are developed. The proposed methodology implements logic, arithmetic, and memory elements by considering performance parameters such as area, delay, power dissipation, and reliability. With combination of logic, arithmetic, and memory elements a synchronous state machine (SSM), representation of a computer, is realized. The proposed methodology targets variety of emerging technologies including nanowire/nanotube crossbar arrays, magnetic switch-based structures, and crossbar memories. The results of this project will be a foundation of nano-crossbar based circuit design techniques and greatly contribute to the construction of emerging computers beyond CMOS. The topic of this project can be considered under the research area of â\u80\u9cEmerging Computing Modelsâ\u80\u9d or â\u80\u9cComputational Nanoelectronicsâ\u80\u9d, more specifically the design, modeling, and simulation of new nanoscale switches beyond CMOS

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201

Custom Integrated Circuits

Contains reports on ten research projects.Analog Devices, Inc.IBM CorporationNational Science Foundation/Defense Advanced Research Projects Agency Grant MIP 88-14612Analog Devices Career Development Assistant ProfessorshipU.S. Navy - Office of Naval Research Contract N0014-87-K-0825AT&TDigital Equipment CorporationNational Science Foundation Grant MIP 88-5876

Intelligent optimization of Circuit placement on FPGA

Field programmable gate arrays (FPGAs) have revolutionized the way digital systems are designed and built over the past decade. With architectures capable of holding tens of millions of logic gates on the horizon and planned integration of configurable logic into system-on-chip platforms, the versatility of programmable devices expected to increase dramatically. Placement is one of the vital steps in mapping a design into FPGA in order to take best advantage of the resources and flexibility provided by it. Here, we propose to test techniques of Placement Optimization on MCNC Benchmark circuits. PSO (Particle Swarm Optimization) has been implemented on circuit netlist with bounding box as cost function. Alternate cost functions were also employed to verify efficiency of optimization. Furthermore, lazy descent was introduced into the algorithm to impede premature convergence. Different values of acceleration and weighing factors were used in the implementation and corresponding convergence results were analyzed. Keywords- FPGA Placement; Particle Swarm Optimization; MCNC Benchmarks Circuits; Bounding Box driven Placement