Energy efficiency enhancements for semiconductors, communications, sensors and software achieved in cool silicon cluster project

An overview about the German cluster project Cool Silicon aiming at increasing the energy efficiency for semiconductors, communications, sensors and software is presented. Examples for achievements are: 1000 times reduced gate leakage in transistors using high-fc (HKMG) materials compared to conventional poly-gate (SiON) devices at the same technology node; 700 V transistors integrated in standard 0.35 µm CMOS; solar cell efficiencies above 19% at < 200 W/m2 irradiation; 0.99 power factor, 87% efficiency and 0.088 distortion factor for dc supplies; 1 ns synchronization resolution via Ethernet; database accelerators allowing 85% energy savings for servers; adaptive software yielding energy reduction of 73% for e-Commerce applications; processors and corresponding data links with 40% and 70% energy savings, respectively, by adaption of clock frequency and supply voltage in less than 20 ns; clock generator chip with tunable frequency from 83-666 MHz and 0.62-1.6 mW dc po wer; 90 Gb/s on-chip link over 6 mm and efficiency of 174 fJ/mm; dynamic biasing system doubling efficiency in power amplifiers; 60 GHz BiCMOS frontends with dc power to bandwidth ratio of 0.17 mW/MHz; driver assistance systems reducing energy consumption by 10% in cars

The statistical geometry of transcriptome divergence in cell-type evolution and cancer

In evolution, body plan complexity increases due to an increase in the number of individualized cell types. Yet, there is very little understanding of the mechanisms that produce this form of organismal complexity. One model for the origin of novel cell types is the sister cell-type model. According to this model, each cell type arises together with a sister cell type through specialization from an ancestral cell type. A key prediction of the sister cell-type model is that gene expression profiles of cell types exhibit tree structure. Here we present a statistical model for detecting tree structure in transcriptomic data and apply it to transcriptomes from ENCODE and FANTOM5. We show that transcriptomes of normal cells harbour substantial amounts of hierarchical structure. In contrast, cancer cell lines have less tree structure, suggesting that the emergence of cancer cells follows different principles from that of evolutionary cell-type origination