An advanced Framework for efficient IC optimization based on analytical models engine

En base als reptes sorgits a conseqüència de l'escalat de la tecnologia, la present tesis desenvolupa i analitza un conjunt d'eines orientades a avaluar la sensibilitat a la propagació d'esdeveniments SET en circuits microelectrònics. S'han proposant varies mètriques de propagació de SETs considerant l'impacto dels emmascaraments lògic, elèctric i combinat lògic-elèctric. Aquestes mètriques proporcionen una via d'anàlisi per quantificar tant les regions més susceptibles a propagar SETs com les sortides més susceptibles de rebre'ls. S'ha desenvolupat un conjunt d'algorismes de cerca de camins sensibilitzables altament adaptables a múltiples aplicacions, un sistema lògic especific i diverses tècniques de simplificació de circuits. S'ha demostrat que el retard d'un camí donat depèn dels vectors de sensibilització aplicats a les portes que formen part del mateix, essent aquesta variació de retard comparable a la atribuïble a les variacions paramètriques del proces.En base a los desafíos surgidos a consecuencia del escalado de la tecnología, la presente tesis desarrolla y analiza un conjunto de herramientas orientadas a evaluar la sensibilidad a la propagación de eventos SET en circuitos microelectrónicos. Se han propuesto varias métricas de propagación de SETs considerando el impacto de los enmascaramientos lógico, eléctrico y combinado lógico-eléctrico. Estas métricas proporcionan una vía de análisis para cuantificar tanto las regiones más susceptibles a propagar eventos SET como las salidas más susceptibles a recibirlos. Ha sido desarrollado un conjunto de algoritmos de búsqueda de caminos sensibilizables altamente adaptables a múltiples aplicaciones, un sistema lógico especifico y diversas técnicas de simplificación de circuitos. Se ha demostrado que el retardo de un camino dado depende de los vectores de sensibilización aplicados a las puertas que forman parte del mismo, siendo esta variación de retardo comparable a la atribuible a las variaciones paramétricas del proceso.Based on the challenges arising as a result of technology scaling, this thesis develops and evaluates a complete framework for SET propagation sensitivity. The framework comprises a number of processing tools capable of handling circuits with high complexity in an efficient way. Various SET propagation metrics have been proposed considering the impact of logic, electric and combined logic-electric masking. Such metrics provide a valuable vehicle to grade either in-circuit regions being more susceptible of propagating SETs toward the circuit outputs or circuit outputs more susceptible to produce SET. A quite efficient and customizable true path finding algorithm with a specific logic system has been constructed and its efficacy demonstrated on large benchmark circuits. It has been shown that the delay of a path depends on the sensitization vectors applied to the gates within the path. In some cases, this variation is comparable to the one caused by process parameters variation

High speed event-based visual processing in the presence of noise

Standard machine vision approaches are challenged in applications where large amounts of noisy temporal data must be processed in real-time. This work aims to develop neuromorphic event-based processing systems for such challenging, high-noise environments. The novel event-based application-focused algorithms developed are primarily designed for implementation in digital neuromorphic hardware with a focus on noise robustness, ease of implementation, operationally useful ancillary signals and processing speed in embedded systems

An efficient logic fault diagnosis framework based on effect-cause approach

Fault diagnosis plays an important role in improving the circuit design process and the manufacturing yield. With the increasing number of gates in modern circuits, determining the source of failure in a defective circuit is becoming more and more challenging. In this research, we present an efficient effect-cause diagnosis framework for combinational VLSI circuits. The framework consists of three stages to obtain an accurate and reasonably precise diagnosis. First, an improved critical path tracing algorithm is proposed to identify an initial suspect list by backtracing from faulty primary outputs toward primary inputs. Compared to the traditional critical path tracing approach, our algorithm is faster and exact. Second, a novel probabilistic ranking model is applied to rank the suspects so that the most suspicious one will be ranked at or near the top. Several fast filtering methods are used to prune unrelated suspects. Finally, to refine the diagnosis, fault simulation is performed on the top suspect nets using several common fault models. The difference between the observed faulty behavior and the simulated behavior is used to rank each suspect. Experimental results on ISCAS85 benchmark circuits show that this diagnosis approach is efficient both in terms of memory space and CPU time and the diagnosis results are accurate and reasonably precise

Intrinsically Evolvable Artificial Neural Networks

Dedicated hardware implementations of neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, most custom hardware implementations do not support intrinsic training of these networks on-chip. The training is typically done using offline software simulations and the obtained network is synthesized and targeted to the hardware offline. The FPGA design presented here facilitates on-chip intrinsic training of artificial neural networks. Block-based neural networks (BbNN), the type of artificial neural networks implemented here, are grid-based networks neuron blocks. These networks are trained using genetic algorithms to simultaneously optimize the network structure and the internal synaptic parameters. The design supports online structure and parameter updates, and is an intrinsically evolvable BbNN platform supporting functional-level hardware evolution. Functional-level evolvable hardware (EHW) uses evolutionary algorithms to evolve interconnections and internal parameters of functional modules in reconfigurable computing systems such as FPGAs. Functional modules can be any hardware modules such as multipliers, adders, and trigonometric functions. In the implementation presented, the functional module is a neuron block. The designed platform is suitable for applications in dynamic environments, and can be adapted and retrained online. The online training capability has been demonstrated using a case study. A performance characterization model for RC implementations of BbNNs has also been presented

Complex Dynamics in Digital Nonlinear Oscillators: Experimental Analysis and Verification

A specific topology of Digital Nonlinear Oscillators (DNOs) has been implemented by using commercial off-the-shelf digital components to experimentally verify and demonstrate the capability of these circuits to support complex dynamics, independently from their implementation technology. In detail, a direct experimental evidence of the DNO dynamical behavior is presented at the analog level with a bifurcation diagram analysis, investigation of periodic and chaotic attractors, and dynamical stability. The autonomous circuit has been investigated as a source of entropy, adopting different figures of merit, including the Lempel–Ziv Complexity, to evaluate the dynamics measured under different operating conditions

34th Midwest Symposium on Circuits and Systems-Final Program

Organized by the Naval Postgraduate School Monterey California. Cosponsored by the IEEE Circuits and Systems Society. Symposium Organizing Committee: General Chairman-Sherif Michael, Technical Program-Roberto Cristi, Publications-Michael Soderstrand, Special Sessions- Charles W. Therrien, Publicity: Jeffrey Burl, Finance: Ralph Hippenstiel, and Local Arrangements: Barbara Cristi