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Preprint Aug 2026

APT: Accelerating Diffusion Transformers via Attention Probability-Guided Pruning and Quantization

APT, a software-hardware co-designed accelerator for high-resolution DiTs that leverages attention probabilities as a unified importance metric to jointly optimize computation through fine-grained pruning and adaptive precision scaling, and is evaluated on SOTA DiT models including PixArt, Stable Diffusion 3, and FLUX.

Sungyeob Yoo, Seeyeon Kim, Joonyong Park et al. · 0 citations
Open access Aug 2026

A Memory-Efficient Depthwise Separable Convolution Accelerator Using Run-Length Coding

A memory-efficient hardware accelerator for depthwise separable convolution that minimizes off-chip memory traffic and parameter storage and performs the depthwise separable convolution with a small number of logic resources and on-chip memory, confirming its feasibility for resource-constrained edge devices.

Jaeseong Kim, Taehong Min, Chaebin Lee et al. · 0 citations
Jul 2026

DSTAR: Accelerating Diffusion Transformers via Spatial and Temporal Redundancy Reduction

DSTAR, a software-hardware co-design framework that accelerates DiT inference by reducing spatial and temporal redundancy and incorporates a sparse attention reuse mechanism to minimize redundant computation in attention layers, and design a specialized hardware accelerator which achieves high efficiency in both latenc...

Chi Zhang, Jieru Zhao, Yu Feng et al. · 2 citations
Open access Aug 2026

FNO-Speed: Accelerate Fourier Neural Operators Training for Solving PDEs

FNO-Speed, an integrated solution incorporating the multi-level parallel FNO-aware mapping and tiling GEMM optimization strategy and the custom-sized high-frequency signal filtering scheme, is proposed, fully demonstrating the effectiveness of the FNO-Speed optimization strategy in improving FNO performance.

Tao Song, Ying Ouyang, Xiangyu Meng et al. · 0 citations
Jul 2026

FeatFix: Reuse What You Verify through Local Exact-Feature Correction for Faster Cached Diffusion Inference

FeatFix is introduced, a local exact-feature correction method for cached diffusion inference that replaces the complete draft block output with the exact output computed from the same incoming state, avoiding token- or channel-level partial replacement and full-timestep recomputation.

Han-Shuai Cui, Zhiqing Tang, Z. Yao et al. · 0 citations
Preprint Aug 2026

Fast and Memory-Efficient Wavelet Convolutions via I/O-Aware Reformulation

The reformulation preserves the benefits of WTConv while substantially reducing its execution time and memory footprint, removing the systems overhead that previously limited its practical efficiency.

Amit Aflalo, Shahaf E. Finder, Roy Amoyal et al. · 0 citations

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MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

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