Data Processing and Information Classification-An In-Memory Approach.

To live in the information society means to be surrounded by billions of electronic devices full of sensors that constantly acquire data. This enormous amount of data must be processed and classified. A solution commonly adopted is to send these data to server farms to be remotely elaborated. The drawback is a huge battery drain due to high amount of information that must be exchanged. To compensate this problem data must be processed locally, near the sensor itself. But this solution requires huge computational capabilities. While microprocessors, even mobile ones, nowadays have enough computational power, their performance are severely limited by the Memory Wall problem. Memories are too slow, so microprocessors cannot fetch enough data from them, greatly limiting their performance. A solution is the Processing-In-Memory (PIM) approach. New memories are designed that can elaborate data inside them eliminating the Memory Wall problem. In this work we present an example of such a system, using as a case of study the Bitmap Indexing algorithm. Such algorithm is used to classify data coming from many sources in parallel. We propose a hardware accelerator designed around the Processing-In-Memory approach, that is capable of implementing this algorithm and that can also be reconfigured to do other tasks or to work as standard memory. The architecture has been synthesized using CMOS technology. The results that we have obtained highlights that, not only it is possible to process and classify huge amount of data locally, but also that it is possible to obtain this result with a very low power consumption.

Morpho-structural and physiological response of container-grown Sangiovese and Montepulciano cvv. (Vitis vinifera) to re-watering after a pre-veraison limiting water deficit.

A better physiological and productive performance of cv. Montepulciano versus cv. Sangiovese under well-watered conditions has been recently assessed. The objective of this study was to verify that this behaviour is maintained when a pre-veraison deficit irrigation (vines held at 40% pot capacity from fruit-set to veraison) followed by re-watering (pot capacity reported at 90%). Single leaf assimilation rate and stomatal conductance, diurnal and seasonal whole-canopy net CO2 exchange (NCER) and water use efficiency were always higher in Sangiovese under deficit irrigation. Due to water shortage Montepulciano displayed a more compact growing habit due to decreased shoot and internode length. Sangiovese showed excellent recovery upon re-watering as NCER resulted to be higher than the pre-stress period; however, this might also relate to early and severe basal leaf yellowing and shedding. Early deficit irrigation affected xylem characteristics of Montepulciano more than in Sangiovese; vessel density increased (37 vs 29%, respectively, compared with well-watered vines) and the hydraulic conductance decreased more (-13 vs -3% respectively) compared with well-watered vines. Yield components and technological maturity were similar in the two cultivars, whereas Montepulciano grapes had lower anthocyanins and phenolics. Higher physiological and productive efficiency under non-limiting water conditions showed by Montepulciano compared with Sangiovese was basically reversed when both cultivars were subjected to an early deficit irrigation.