COMPASS – A tool for evaluation of compression strategies for embedded processors

A major concern of embedded system architects is the design for low power. We address one aspect of the problem in this paper, namely the effect of executable code compression. There are two benefits of code compression – firstly, a reduction in the memory footprint of embedded software, and secondly, potential reduction in memory bus traffic and power consumption. Since decompression has to be performed at run time it is achieved by hardware. We describe a tool called COMPASS which can evaluate a range of strategies for any given set of benchmarks and display compression ratios. Also, given an execution trace, it can compute the effect on bus toggles, and cache misses for a range of compression strategies. The tool is interactive and allows the user to vary a set of parameters, and observe their effect on performance. We describe an implementation of the tool and demonstrate its effectiveness. To the best of our knowledge this is the first tool proposed for such a purpose.

Construction and use of linear regression models for processor performance analysis

Processor architects have a challenging task of evaluating a large design space consisting of several interacting parameters and optimizations. In order to assist architects in making crucial design decisions, we build linear regression models that relate Processor performance to micro-architecture parameters, using simulation based experiments. We obtain good approximate models using an iterative process in which Akaike's information criteria is used to extract a good linear model from a small set of simulations, and limited further simulation is guided by the model using D-optimal experimental designs. The iterative process is repeated until desired error bounds are achieved. We used this procedure to establish the relationship of the CPI performance response to 26 key micro-architectural parameters using a detailed cycle-by-cycle superscalar processor simulator The resulting models provide a significance ordering on all micro-architectural parameters and their interactions, and explain the performance variations of micro-architectural techniques.

Current State of Domestic Concealed Wiring Practices in Bangalore, India

In a modernising world, building and construction trends in recent urban centres such as Bangalore, set precedence for developments in other urban centres of the country. Under such conditions, evaluating the current state of building practices could prove useful for identifying the likely nature of nationwide building trends. This paper comprises a study to evaluate the current state of domestic concealed wiring practices in the context of a modern urban centre area in India. Presently, concealed wiring is the predominant wiring method adopted for residences, both bungalows and apartments. A modern residential block in the city of Bangalore (India) was chosen as the study area. The study included extensive interaction and surveys amongst residents, professionals (architects and engineers) and site personnel (contractors and electricians). In addition, the study also included site verification on the state of wiring practices in the residential block. The study indicates that while aesthetics was the prime reason that dictated the choice of concealed wiring, its effectiveness as an appropriate and safe wiring method is severely compromised. Details of the study, results and recommendations are presented in this paper.

INTACTE: An Interconnect Area, Delay, and Energy Estimation Tool for Microarchitectural Explorations

Prior work on modeling interconnects has focused on optimizing the wire and repeater design for trading off energy and delay, and is largely based on low level circuit parameters. Hence these models are hard to use directly to make high level microarchitectural trade-offs in the initial exploration phase of a design. In this paper, we propose INTACTE, a tool that can be used by architects toget reasonably accurate interconnect area, delay, and power estimates based on a few architecture level parameters for the interconnect such as length, width (in number of bits), frequency, and latency for a specified technology and voltage. The tool uses well known models of interconnect delay and energy taking into account the wire pitch, repeater size, and spacing for a range of voltages and technologies.It then solves an optimization problem of finding the lowest energy interconnect design in terms of the low level circuit parameters, which meets the architectural constraintsgiven as inputs. In addition, the tool also provides the area, energy, and delay for a range of supply voltages and degrees of pipelining, which can be used for micro-architectural exploration of a chip. The delay and energy models used by the tool have been validated against low level circuit simulations. We discuss several potential applications of the tool and present an example of optimizing interconnect design in the context of clustered VLIW architectures. Copyright 2007 ACM.

A Predictive Perfomance Model for Superscalar Processors

Designing and optimizing high performance microprocessors is an increasingly difficult task due to the size and complexity of the processor design space, high cost of detailed simulation and several constraints that a processor design must satisfy. In this paper, we propose the use of empirical non-linear modeling techniques to assist processor architects in making design decisions and resolving complex trade-offs. We propose a procedure for building accurate non-linear models that consists of the following steps: (i) selection of a small set of representative design points spread across processor design space using latin hypercube sampling, (ii) obtaining performance measures at the selected design points using detailed simulation, (iii) building non-linear models for performance using the function approximation capabilities of radial basis function networks, and (iv) validating the models using an independently and randomly generated set of design points. We evaluate our model building procedure by constructing non-linear performance models for programs from the SPEC CPU2000 benchmark suite with a microarchitectural design space that consists of 9 key parameters. Our results show that the models, built using a relatively small number of simulations, achieve high prediction accuracy (only 2.8% error in CPI estimates on average) across a large processor design space. Our models can potentially replace detailed simulation for common tasks such as the analysis of key microarchitectural trends or searches for optimal processor design points.