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**1 - 2**of**2**### High-Level Synthesis of Analog Sensor Interface Front-Ends

- Proc. ED&TC 97
, 1997

"... In this paper we will compare three different methodologies for analog high-level synthesis. Two optimizationbased methods---one with simulations in the loop, the other with equations---and a library-based approach are discussed and illustrated with experimental results. The comparison is made by me ..."

Abstract
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In this paper we will compare three different methodologies for analog high-level synthesis. Two optimizationbased methods---one with simulations in the loop, the other with equations---and a library-based approach are discussed and illustrated with experimental results. The comparison is made by means of a real life design example--- a radiation detector interface ASIC---although the methodologies presented in this paper, are generally applicable. 1 Introduction Advances in VLSI technology allow the integration of very complex signal processing systems on a single ASIC. In many cases these systems in silicon contain analog and digital signal processing circuits as well as digital controllers on the same die. An important application domain for these mixed analog/digital integrated systems are sensor interfaces. Although there is a strong trend towards implementing as much signal processing functionality as possible in the digital domain, analog circuits will always play an important...

### 1.2 Distinct SA approaches

, 1061

"... Given a combinatorial optimization problem specified by a finite set of configurations or states S and by a cost function C defined on all the states j in S, the SA algorithm is characterized by a rule to generate randomly a new configuration with a certain probability, and by a random acceptance ru ..."

Abstract
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Given a combinatorial optimization problem specified by a finite set of configurations or states S and by a cost function C defined on all the states j in S, the SA algorithm is characterized by a rule to generate randomly a new configuration with a certain probability, and by a random acceptance rule according to which the new configuration is accepted or rejected. A parameter T controls the acceptance rule. The generic structure of the algorithm is presented in Fig.1. Theoretical investigations of the SA optimization technique have been reported in some literatures [8].