Analysis of Lifetime of Large Wireless Sensor Networks Based on Multiple Battery Levels

Due to the limited transmission range, data sensed by each sensor has to be forwarded in a multi-hop fashion before being delivered to the sink. The sensors closer to the sink have to forward comparatively more messages than sensors at the periphery of the network,and will deplete their batteries earlier. Besides the loss of the sensing capabilities of the nodes close to the sink, a more serious consequence of the death of the first tier of sensor nodes is the loss of connectivity between the nodes at the periphery of the network and the sink;it makes the wireless networks expire. To alleviate this undesired effect and maximize the useful lifetime of the network, we investigate the energy consumption of different tiers and the effect of multiple battery levels, and demonstrate an attractively simple scheme to redistribute the total energy budget in multiple battery levels by data traffic load. We show by theoretical analysis, as well as simulation, that this substantially improves the network lifetime.

PRAP-PIM:A weight pattern reusing aware pruning method for ReRAM-based PIM DNN accelerators

Resistive Random-Access Memory(ReRAM)based Processing-in-Memory(PIM)frameworks are proposed to accelerate the working process of DNN models by eliminating the data movement between the computing and memory units.To further mitigate the space and energy consumption,DNN model weight sparsity and weight pattern repetition are exploited to optimize these ReRAM-based accelerators.However,most of these works only focus on one aspect of this software/hardware codesign framework and optimize them individually,which makes the design far from optimal.In this paper,we propose PRAP-PIM,which jointly exploits the weight sparsity and weight pattern repetition by using a weight pattern reusing aware pruning method.By relaxing the weight pattern reusing precondition,we propose a similarity-based weight pattern reusing method that can achieve a higher weight pattern reusing ratio.Experimental results show that PRAP-PIM achieves 1.64×performance improvement and 1.51×energy efficiency improvement in popular deep learning benchmarks,compared with the state-of-the-art ReRAM-based DNN accelerators.

Preparation of high concentration polyaluminum chloride by chemical synthesis-membrane distillation method with self-made hollow fiber membrane

A method of direct contact membrane distillation （DCMD） with a self-made hollow polyvinylidene fluoride membrane was applied to prepare high concentration polyaluminum chloride （PACl） with high Alb content based on chemical synthesis. The permeate flux and Al species distribution were investigated. The experimental results showed that the permeate flux decreased from 14 to 6 kg/（m2·hr） at the end of the DCMD process, which can be mainly attributed to the formation of NaCl deposits on the membrane surface. The Alb content decreased slightly, only from 86.3% to 84.4%, when the DCMD experiment finished, correspondingly the Alc content increased slightly from 7.2% to 8.5%, and the Ala content remained at 7% during the whole DCMD process. A PACl with Alb content of 84% at total aluminum concentration 2.2 mol/L was successfully prepared by the chemical synthesis-DCMD method.