Flexible MIPS Soft Processor Architecture

The flexible MIPS soft processor architecture borrows selected technologies from high-performance computing to deliver a modular, highly customizable CPU targeted towards FPGA implementations for embedded systems; the objective is to provide a more flexible architectural alternative to coprocessor-based solutions. The processor performs out-of-order execution on parallel functional units, it delivers in-order instruction commit and it is compatible with the MIPS-1 Instruction Set Architecture. Amongst many available options, the user can introduce custom instructions and matching functional units; modify existing units; change the pipelining depth within functional units to any fixed or variable value; customize instruction definitions in terms of operands, control signals and register file interaction; insert multiple redundant functional units for improved performance. The flexibility provided by the architecture allows the user to expand the processor functionality to implement instructions of coprocessor-level complexity through additional functional units. The processor design was implemented and simulated on two FPGA platforms, tested on multiple applications, and compared to three commercially available soft processor solutions in terms of features, area, clock frequency and benchmark performance

GMM classification of environmental sounds for surveillance applications

This thesis describes an audio event detection system which automatically classifies an impulsive audio event as scream, gunshot, broken glasses or barking dogs with every background noise. The classification system uses four parallel Gaussian Mixture Models (GMMs) classifiers each of which decides if the sound belongs to its class or is only noise. Each classifier is trained using different features, chosen from a set of 40 audio features. Simultaneously the system can detect any kind of impulsive sounds using only one feature with very high precision. The classification system is implemented in the Network-Integrated Multimedia Middleware (NMM) for real-time processing and communications with other surveillance applications. In order to validate the proposed detection algorithm, we carried out extensive experiments (both off-line and real-time) on a hand-made set of sounds mixed with ambient noise at different Signal-to-Noise ratios (SNRs). Our results demonstrate that the system is able to guarantee 70\% of accuracy and 90\% of precision at 0 dB SNR, starting from 100\% of both accuracy and precision with clean sounds at 20 dB SNR. Sommario: Questa tesi descrive un sistema di rilevazione di eventi audio che classifica automaticamente un rumore impulsivo come urla, spari, vetri rotti o cani che abbaiano con qualsiasi rumore di sottofondo. Il sistema di classificazione utilizza quattro classificatori in parallelo, costruiti con i Gaussian Mixture Models (GMMs), ciascuno dei quali decide se il suono appartiene alla propria classe o se \`e soltanto rumore. Ogni classificatore \`e addestrato con differenti feature, scelte da un insieme di 40 feature audio. Contemporaneamente il sistema pu\`o rilevare qualsiasi tipo di suoni impulsivi utilizzando una sola feature con una precisione molto elevata. Il sistema di classificazione \`e implementato nel Network-Integrated Multimedia Middleware (NMM) per l'elaborazione in tempo reale e le comunicazioni con altre applicazioni di sorveglianza. Al fine di validare l'algoritmo di rilevazione proposto, sono stati effettuati vari esperimenti (sia off-line sia in tempo reale) su un personale database di suoni, mescolati con rumore ambientale, a diversi rapporti di segnale-rumore (SNR). I nostri risultati dimostrano che il sistema \`e in grado di garantire il 70\% di accuratezza e il 90\% di precisione a 0 dB di SNR, a partire da 100\% di accuratezza e precisione con suoni puliti a 20 dB di SN

High-Speed Performance, Power and Thermal Co-simulation For SoC Design

This dissertation presents a multi-faceted effort at developing standard System Design Language based tools that allow designers to the model power and thermal behavior of SoCs, including heterogeneous SoCs that include non-digital components. The research contributions made in this dissertation include: • SystemC-based power/performance co-simulation for the Intel XScale microprocessor. We performed detailed characterization of the power dissipation patterns of a variety of system components and used these results to build detailed power models, including a highly accurate, validated instruction-level power model of the XScale processor. We also proposed a scalable, efficient and validated methodology for incorporating fast, accurate power modeling capabilities into system description languages such as SystemC. This was validated against physical measurements of hardware power dissipation. • Modeling the behavior of non-digital SoC components within standard System Design Languages. We presented an approach for modeling the functionality, performance, power, and thermal behavior of a complex class of non-digital components — MEMS microhotplate-based gas sensors — within a SystemC design framework. The components modeled include both digital components (such as microprocessors, busses and memory) and MEMS devices comprising a gas sensor SoC. The first SystemC models of a MEMS-based SoC and the first SystemC models of MEMS thermal behavior were described. Techniques for significantly improving simulation speed were proposed, and their impact quantified. • Vertically Integrated Execution-Driven Power, Performance and Thermal Co-Simulation For SoCs. We adapted the above techniques and used numerical methods to model the system of differential equations that governs on-chip thermal diffusion. This allows a single high-speed simulation to span performance, power and thermal modeling of a design. It also allows feedback behaviors, such as the impact of temperature on power dissipation or performance, to be modeled seamlessly. We validated the thermal equation-solving engine on test layouts against detailed low-level tools, and illustrated the power of such a strategy by demonstrating a series of studies that designers can perform using such tools. We also assessed how simulation and accuracy are impacted by spatial and temporal resolution used for thermal modeling

Creating music by listening

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 127-139).Machines have the power and potential to make expressive music on their own. This thesis aims to computationally model the process of creating music using experience from listening to examples. Our unbiased signal-based solution models the life cycle of listening, composing, and performing, turning the machine into an active musician, instead of simply an instrument. We accomplish this through an analysis-synthesis technique by combined perceptual and structural modeling of the musical surface, which leads to a minimal data representation. We introduce a music cognition framework that results from the interaction of psychoacoustically grounded causal listening, a time-lag embedded feature representation, and perceptual similarity clustering. Our bottom-up analysis intends to be generic and uniform by recursively revealing metrical hierarchies and structures of pitch, rhythm, and timbre. Training is suggested for top-down un-biased supervision, and is demonstrated with the prediction of downbeat. This musical intelligence enables a range of original manipulations including song alignment, music restoration, cross-synthesis or song morphing, and ultimately the synthesis of original pieces.by Tristan Jehan.Ph.D