|Author||Datta, Kushal ♦ Mukherjee, Arindam ♦ Ravindran, Arun|
|Source||ACM Digital Library|
|Publisher||Association for Computing Machinery (ACM)|
|Subject Domain (in DDC)||Computer science, information & general works ♦ Data processing & computer science|
|Subject Keyword||Automatic synmthesis ♦ Optimization|
|Abstract||We present an automated design flow for minimizing the use of diodes and switches (active devices) in design implementations on a nanofabric based on chemically self-assembled electronic nanotechnology as proposed in Goldstein and Budiu . Connectivity and logic in the nanofabric are realized using the switch and diode behaviors of molecular devices, unlike very large scale integrated (VLSI) circuits where complementary metal-oxide semiconductor (CMOS) gates are used. Similar to the optimization goal of reducing the number of gates in VLSI designs to minimize area, power dissipation, and delay, decreasing the number of switches and diodes used in the nanofabric can potentially minimize design implementation area and power dissipation, besides reducing the delay and signal drop between latched stages in order to improve performance. An integrated placement, topology selection, and routing approach for design implementation on the nanofabric is proposed. Note that this problem is fundamentally different from CMOS VLSI placement and routing because of the inherent routing-dependent logic realization in our target nanofabric. To the best of our knowledge this is the first reported work on automated integrated placement, topology selection, and routing for diode-based nanofabrics. A practical and scalable simulated annealing-based placement and routing algorithm has been implemented. On average, the integrated placement and routing approach achieves a reduction of 12% in the number of switches and diodes used for MCNC benchmarks, compared to separate placement and routing optimization results. The maximum reduction achieved in the number of active devices using our approach is 24%, and in general, we observed that the bigger the benchmark, the larger the improvement achieved.|
|Age Range||18 to 22 years ♦ above 22 year|
|Education Level||UG and PG|
|Learning Resource Type||Article|
|Publisher Place||New York|
|Journal||ACM Journal on Emerging Technologies in Computing Systems (JETC)|
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