Power Estimation Technique for DSP Architectures.

The main goal of power estimation is to optimize the power consumption of a electronic design. Power is a strongly pattern dependent function. Input statistics greatly influence on average power. We solve the pattern dependence problem for intellectual property (IP) designs. In this paper, we present a power macro-modeling technique for digital signal processing (DSP) architectures in terms of the statistical knowledge of their primary inputs. During the power estimation procedure, the sequence of an input stream is generated by a genetic algorithm using input metrics. Then, a Monte Carlo zero delay simulation is performed and a power dissipation macro-model function is built from power dissipation results. From then on, this macro-model function can be used to estimate power dissipation of the system just by using the statistics of the macro-block’s primary in puts. In experiments with the DSP system, the average error is 26%

Auto-generation of passive scalable macromodels for microwave components using scattered sequential sampling

This paper presents a method for automatic construction of stable and passive scalable macromodels for parameterized frequency responses. The method requires very little prior knowledge to build the scalable macromodels thereby considerably reducing the burden on the designers. The proposed method uses an efficient scattered sequential sampling strategy with as few expensive simulations as possible to generate accurate macromodels for the system using state-of-the-art scalable macromodeling methods. The scalable macromodels can be used as a replacement model for the actual simulator in overall design processes. Pertinent numerical results validate the proposed sequential sampling strategy

Modeling and Optimization of the Microwave PCB Interconnects Using Macromodel Techniques

L'abstract è presente nell'allegato / the abstract is in the attachmen

DESIGN AUTOMATION FOR LOW POWER RFID TAGS

Radio Frequency Identification (RFID) tags are small, wireless devices capable of automated item identification, used in a variety of applications including supply chain management, asset management, automatic toll collection (EZ Pass), etc. However, the design of these types of custom systems using the traditional methods can take months for a hardware engineer to develop and debug. In this dissertation, an automated, low-power flow for the design of RFID tags has been developed, implemented and validated. This dissertation presents the RFID Compiler, which permits high-level design entry using a simple description of the desired primitives and their behavior in ANSI-C. The compiler has different back-ends capable of targeting microprocessor-based or custom hardware-based tags. For the hardware-based tag, the back-end automatically converts the user-supplied behavior in C to low power synthesizable VHDL optimized for RFID applications. The compiler also integrates a fast, high-level power macromodeling flow, which can be used to generate power estimates within 15% accuracy of industry CAD tools and to optimize the primitives and / or the behaviors, compared to conventional practices. Using the RFID Compiler, the user can develop the entire design in a matter of days or weeks. The compiler has been used to implement standards such as ANSI, ISO 18000-7, 18000-6C and 18185-7. The automatically generated tag designs were validated by targeting microprocessors such as the AD Chips EISC and FPGAs such as Xilinx Spartan 3. The corresponding ASIC implementation is comparable to the conventionally designed commercial tags in terms of the energy and area. Thus, the RFID Compiler permits the design of power efficient, custom RFID tags by a wider audience with a dramatically reduced design cycle

Effective network grid synthesis and optimization for high performance very large scale integration system design

制度:新 ; 文部省報告番号:甲2642号 ; 学位の種類:博士(工学) ; 授与年月日:2008/3/15 ; 早大学位記番号:新480