A Context-Aware Readout System for Sparse Touch Sensing Array Using Ultra-low-power Always-on Event Detection
Increasing demand for larger touch screen panels (TSPs) places more energy burden to mobile systems with conventional sensing methods. To mitigate this problem, taking advantage of the touch event sparsity, this paper proposes a novel TSP readout system that can obtain huge energy saving by turning off the readout circuits when none of the sensors are activated. To this end, a novel ultra-low-power always-on event and region of interest detection based on lightweight compressed sensing is proposed. Exploiting the proposed event detector, the context-aware TSP readout system, which can improve the energy efficiency by up to 42x, is presented.
Code (0)
등록된 구현이 없습니다.
Tasks
compressed sensingEvent DetectionSimilar Papers 제목 키워드 기반
Region-Aware Portrait Retouching with Sparse Interactive Guidance
Portrait retouching aims to improve the aesthetic quality of input portrait photos and especially requires human-region priority. The deep learning-based methods largely elevate the retouching efficiency and provide prom…
Sparse Readout Prism: Explaining Logit-Lens Scores in Features Instead of Tokens
A language model's prediction of its next token develops across layers, and lens methods track this process by decoding intermediate hidden states into tokens. But a lens reading reflects both the hidden state and the re…
When and How Long? The Readout-Mediator Angle in Temporal Reasoning
A linear probe can decode a representation almost perfectly and yet be completely irrelevant to how the model uses it. On calendar-date duration reasoning in language models, a $\sin$/$\cos$ probe recovers day-of-year fr…
Artificial Intelligence Virtual Cells: From Measurements to Decisions across Modality, Scale, Dynamics, and Evaluation
Artificial Intelligence Virtual Cells (AIVCs) aim to learn executable, decision-relevant models of cell state from multimodal, multiscale measurements. Recent studies have introduced single-cell and spatial foundation mo…
INRetouch: Context Aware Implicit Neural Representation for Photography Retouching
Professional photo editing remains challenging, requiring extensive knowledge of imaging pipelines and significant expertise. While recent deep learning approaches, particularly style transfer methods, have attempted to …
Image ReconstructionPhoto RetouchingRaw reconstructionStyle Transfer