296 research outputs found
PuMer: Pruning and Merging Tokens for Efficient Vision Language Models
Large-scale vision language (VL) models use Transformers to perform
cross-modal interactions between the input text and image. These cross-modal
interactions are computationally expensive and memory-intensive due to the
quadratic complexity of processing the input image and text. We present PuMer:
a token reduction framework that uses text-informed Pruning and modality-aware
Merging strategies to progressively reduce the tokens of input image and text,
improving model inference speed and reducing memory footprint. PuMer learns to
keep salient image tokens related to the input text and merges similar textual
and visual tokens by adding lightweight token reducer modules at several
cross-modal layers in the VL model. Training PuMer is mostly the same as
finetuning the original VL model but faster. Our evaluation for two vision
language models on four downstream VL tasks shows PuMer increases inference
throughput by up to 2x and reduces memory footprint by over 50% while incurring
less than a 1% accuracy drop.Comment: Accepted to ACL 2023 Main Conferenc
Robust Sum-Rate Maximization in Transmissive RMS Transceiver-Enabled SWIPT Networks
In this paper, we propose a state-of-the-art downlink communication
transceiver design for transmissive reconfigurable metasurface (RMS)-enabled
simultaneous wireless information and power transfer (SWIPT) networks.
Specifically, a feed antenna is deployed in the transmissive RMS-based
transceiver, which can be used to implement beamforming. According to the
relationship between wavelength and propagation distance, the spatial
propagation models of plane and spherical waves are built. Then, in the case of
imperfect channel state information (CSI), we formulate a robust system
sum-rate maximization problem that jointly optimizes RMS transmissive
coefficient, transmit power allocation, and power splitting ratio design while
taking account of the non-linear energy harvesting model and outage probability
criterion. Since the coupling of optimization variables, the whole optimization
problem is non-convex and cannot be solved directly. Therefore, the alternating
optimization (AO) framework is implemented to decompose the non-convex original
problem. In detail, the whole problem is divided into three sub-problems to
solve. For the non-convexity of the objective function, successive convex
approximation (SCA) is used to transform it, and penalty function method and
difference-of-convex (DC) programming are applied to deal with the non-convex
constraints. Finally, we alternately solve the three sub-problems until the
entire optimization problem converges. Numerical results show that our proposed
algorithm has convergence and better performance than other benchmark
algorithms
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