This paper presents a radiation-hardened flip-flop called MSIFF. The sensitive node-pairs between the master and slave latches are readjusted at layout-level. It obtains a high SEU tolerance with slight area and performance degradation. A test chip was irradiated with the static and dynamic test modes to validate the SEU tolerance of the proposed MSIFF. Experimental results demonstrate superior performance of the MSIFF over the conventional DICE and D flip-flop.
P-hit and N-hit single-event transients are investigated using heavy ion microbeam. A novel layout placement was implemented in the test chip to distinguish SETs originating from P-hit and N-hit. Experimental results indicate both the P-hit and N-hit SETs show an exponentiallike distribution in all target circuits. The SET cross sections and the average pulse width for P-hit and N-hit are also investigated. The well process, the transistor size and the layout topology significantly impact on the cross sections. Only the transistor size impacts on the average pulse width at low LET.
Recently transformer-based ranking models have been shown to deliver high relevance for document search and the relevanceefficiency tradeoff becomes important for fast query response times. This paper presents BECR (BERT-based Composite Re-Ranking), a lightweight composite re-ranking scheme that combines deep contextual token interactions and traditional lexical term-matching features. BECR conducts query decomposition and composes a query representation using pre-computable token embeddings based on uni-grams and skip-n-grams, to seek a tradeoff of inference efficiency and relevance. Thus it does not perform expensive transformer computations during online inference, and does not require the use of GPU. This paper describes an evaluation of relevance and efficiency of BECR with several TREC datasets.
CCS Concepts• Information systems → Learning to rank.
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