This paper investigates and analyzes the resistive averaging network and interpolation technique to estimate the power consumption of preamplifier arrays in a flash analog-to-digital converter (ADC). By comparing the relative power consumption of various configurations, flash ADC designers can select the most power efficient architecture when the operation speed and resolution of a flash ADC are specified. Based on the quantitative analysis, a compact 5-bit flash ADC is designed and fabricated in a 0.13-µm CMOS process. The proposed ADC consumes 180 mW from a 1.2-V supply and occupies 0.16-mm2 active area. Operating at 3.2 GS/s, the ENOB is 4.44 bit and ERBW 1.65 GHz. At 4.2 GS/s, the ENOB is 4.20 bit and ERBW 1.75 GHz. This ADC achieves FOMs of 2.59 and 2.80 pJ/conversion-step at 3.2 and 4.2 GS/s, respectively.

Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly in inferring a new user's consumption preference. Our model can also learn meaningful consumption-specific topics automatically.