Deep-PowerX: A Deep Learning-Based Framework for Low-Power Approximate Logic Synthesis

This paper aims at integrating three powerful techniques namely Deep Learning, Approximate Computing, and Low Power Design into a strategy to optimize logic at the synthesis level. We utilize advances in deep learning to guide an approximate logic synthesis engine to minimize the dynamic power consumption of a given digital CMOS circuit, subject to a predetermined error rate at the primary outputs. Our framework, Deep-PowerX, focuses on replacing or removing gates on a technology-mapped network and uses a Deep Neural Network (DNN) to predict error rates at primary outputs of the circuit when a specific part of the netlist is approximated. The primary goal of Deep-PowerX is to reduce the dynamic power whereas area reduction serves as a secondary objective. Using the said DNN, Deep-PowerX is able to reduce the exponential time complexity of standard approximate logic synthesis to linear time. Experiments are done on numerous open source benchmark circuits. Results show significant reduction in power and area by up to 1.47 times and 1.43 times compared to exact solutions and by up to 22% and 27% compared to state-of-the-art approximate logic synthesis tools while having orders of magnitudes lower run-time

Coarse-Graining and Self-Dissimilarity of Complex Networks

Can complex engineered and biological networks be coarse-grained into smaller and more understandable versions in which each node represents an entire pattern in the original network? To address this, we define coarse-graining units (CGU) as connectivity patterns which can serve as the nodes of a coarse-grained network, and present algorithms to detect them. We use this approach to systematically reverse-engineer electronic circuits, forming understandable high-level maps from incomprehensible transistor wiring: first, a coarse-grained version in which each node is a gate made of several transistors is established. Then, the coarse-grained network is itself coarse-grained, resulting in a high-level blueprint in which each node is a circuit-module made of multiple gates. We apply our approach also to a mammalian protein-signaling network, to find a simplified coarse-grained network with three main signaling channels that correspond to cross-interacting MAP-kinase cascades. We find that both biological and electronic networks are 'self-dissimilar', with different network motifs found at each level. The present approach can be used to simplify a wide variety of directed and nondirected, natural and designed networks.Comment: 11 pages, 11 figure

Using graph concepts to understand the organization of complex systems

Complex networks are universal, arising in fields as disparate as sociology, physics, and biology. In the past decade, extensive research into the properties and behaviors of complex systems has uncovered surprising commonalities among the topologies of different systems. Attempts to explain these similarities have led to the ongoing development and refinement of network models and graph-theoretical analysis techniques with which to characterize and understand complexity. In this tutorial, we demonstrate through illustrative examples, how network measures and models have contributed to the elucidation of the organization of complex systems.Comment: v(1) 38 pages, 7 figures, to appear in the International Journal of Bifurcation and Chaos v(2) Line spacing changed; now 23 pages, 7 figures, to appear in the Special Issue "Complex Networks' Structure and Dynamics'' of the International Journal of Bifurcation and Chaos (Volume 17, Issue 7, July 2007) edited by S. Boccaletti and V. Lator

CycSAT-Unresolvable Cyclic Logic Encryption Using Unreachable States

Logic encryption has attracted much attention due to increasing IC design costs and growing number of untrusted foundries. Unreachable states in a design provide a space of flexibility for logic encryption to explore. However, due to the available access of scan chain, traditional combinational encryption cannot leverage the benefit of such flexibility. Cyclic logic encryption inserts key-controlled feedbacks into the original circuit to prevent piracy and overproduction. Based on our discovery, cyclic logic encryption can utilize unreachable states to improve security. Even though cyclic encryption is vulnerable to a powerful attack called CycSAT, we develop a new way of cyclic encryption by utilizing unreachable states to defeat CycSAT. The attack complexity of the proposed scheme is discussed and its robustness is demonstrated

Automatisierung des Entwurfs vollständig testbarer Schaltungen

Die Kosten für die Testvorbereitung, Testerzeugung und Testdurchführung wachsen überproportional mit der Komplexität anwendungsspezifischer Schaltungen, und die Teststrategie sollte daher bereits in einer sehr frühen Phase des Schaltungsentwurfs festgelegt und berücksichtigt werden. In diesem Artikel werden logische Grundzellen und Algorithmen zur Unterstützung des pseudo-erschöpfenden Tests vorgestellt. Diese Teststrategie hat den Vorteil, daß die äußerst rechenzeitaufwendige Testmustererzeugung entfällt und zugleich eine vollständige Fehlererfassung auf Gatterebene garantiert ist. Die vorgestellten Grundzellen dienen der Zerlegung der Gesamtschaltung in erschöpfend testbare Teile, die präsentierten Algorithmen sollen diese Segmentierungszellen so plazieren, daß der Mehraufwand an Silizium gering bleibt. Hierzu wurden Varianten sogenannter "Hill-Climbing" und "Simulated-Annealing"-Verfahren entwickelt

Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits

Nervous systems are information processing networks that evolved by natural selection, whereas very large scale integrated (VLSI) computer circuits have evolved by commercially driven technology development. Here we follow historic intuition that all physical information processing systems will share key organizational properties, such as modularity, that generally confer adaptivity of function. It has long been observed that modular VLSI circuits demonstrate an isometric scaling relationship between the number of processing elements and the number of connections, known as Rent's rule, which is related to the dimensionality of the circuit's interconnect topology and its logical capacity. We show that human brain structural networks, and the nervous system of the nematode C. elegans, also obey Rent's rule, and exhibit some degree of hierarchical modularity. We further show that the estimated Rent exponent of human brain networks, derived from MRI data, can explain the allometric scaling relations between gray and white matter volumes across a wide range of mammalian species, again suggesting that these principles of nervous system design are highly conserved. For each of these fractal modular networks, the dimensionality of the interconnect topology was greater than the 2 or 3 Euclidean dimensions of the space in which it was embedded. This relatively high complexity entailed extra cost in physical wiring: although all networks were economically or cost-efficiently wired they did not strictly minimize wiring costs. Artificial and biological information processing systems both may evolve to optimize a trade-off between physical cost and topological complexity, resulting in the emergence of homologous principles of economical, fractal and modular design across many different kinds of nervous and computational networks

Recording and Playback of Collaborative Desktops on the Internet

This paper presents a Tcl/Tk recording/playback architecture and implementation that records, plays back and executes a Tcl/Tk collaborative Internet-based desktop. Specifically, the desktop brings together distributed data, application workflows, and teams into collaborative sessions in which the control of the desktop editing and execution is shared. A typical workflow invokes distributed tools and data to support the design of microelectronic systems. We argue that recording and playback of collaborative user interactions can have a wide-range of applications, such as: `keeping minutes' of interactive discussions, clicks of menuspecific commands associated with different tools on the shared desktop, user-entered data and control inputs, user-queried data outputs, support for automated software documentation, tutorials, collaborative playback of tutorials and solutions recorded earlier, etc. The summary of 540 Internet-based experiments, each relying on RecordTaker and PlaybackMaker ..

OmniDesk and OmniFlows: A Platform-Independent Executable and User-Reconfigurable Desktops and Workflows on the Internet

Today, web browsers provide a convenient access to the Internet while (1) increasing the number of useful desktop functions, and, (2) reducing the platform dependence on the operating system of the host. This paper introduces OmniDesk, implemented as an applet, that creates a userconfigurable desktop within the web browser window. User can place any number of objects onto the OmniDesk, ranging from windows that display the contents of a directory or a file on a remote host, to OmniFlow applets that can execute any sequence of user-defined and data-dependent tasks. Identical versions of OmniDesk and a variety of OmniFlow class libraries can be mirrored on several web sites or can be installed locally for faster access and execution. An OmniFlow is a user-created directed dependency graph of data, program, decision, and OmniFlow nodes. Data and program nodes may reside anywhere on the Internet. The proposed approach has a number of advantages over the current html-form-based execution o..