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Found 18 papers, 4 papers with code
PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Prediction
We are the first work to apply graph structure to deep-learning based dynamic IR drop prediction method.
ProPD: Dynamic Token Tree Pruning and Generation for LLM Parallel Decoding
Additionally, it introduces a dynamic token tree generation algorithm to balance the computation and parallelism of the verification phase in real-time and maximize the overall efficiency across different batch sizes, sequence lengths, and tasks, etc.
ASCEND: Accurate yet Efficient End-to-End Stochastic Computing Acceleration of Vision Transformer
To overcome the compatibility challenges, ASCEND proposes a novel deterministic SC block for GELU and leverages an SC-friendly iterative approximate algorithm to design an accurate and efficient softmax circuit.
AttentionLego: An Open-Source Building Block For Spatially-Scalable Large Language Model Accelerator With Processing-In-Memory Technology
Large language models (LLMs) with Transformer architectures have become phenomenal in natural language processing, multimodal generative artificial intelligence, and agent-oriented artificial intelligence.
FS-BAND: A Frequency-Sensitive Banding Detector
Banding artifact, as known as staircase-like contour, is a common quality annoyance that happens in compression, transmission, etc.
BAND-2k: Banding Artifact Noticeable Database for Banding Detection and Quality Assessment
The quality score of a banding image is generated by pooling the banding detection maps masked by the spatial frequency filters.
Memory-aware Scheduling for Complex Wired Networks with Iterative Graph Optimization
Memory-aware network scheduling is becoming increasingly important for deep neural network (DNN) inference on resource-constrained devices.
Falcon: Accelerating Homomorphically Encrypted Convolutions for Efficient Private Mobile Network Inference
Efficient networks, e. g., MobileNetV2, EfficientNet, etc, achieves state-of-the-art (SOTA) accuracy with lightweight computation.
Reducing operator complexity in Algebraic Multigrid with Machine Learning Approaches
We propose a data-driven and machine-learning-based approach to compute non-Galerkin coarse-grid operators in algebraic multigrid (AMG) methods, addressing the well-known issue of increasing operator complexity.
Regression-Oriented Knowledge Distillation for Lightweight Ship Orientation Angle Prediction with Optical Remote Sensing Images
Four tiny SOAP models are also created by replacing the convolutional blocks in Mobile-SOAP with four small-scale networks, respectively.
HybridNet: Dual-Branch Fusion of Geometrical and Topological Views for VLSI Congestion Prediction
Accurate early congestion prediction can prevent unpleasant surprises at the routing stage, playing a crucial character in assisting designers to iterate faster in VLSI design cycles.
EBSR: Enhanced Binary Neural Network for Image Super-Resolution
While the performance of deep convolutional neural networks for image super-resolution (SR) has improved significantly, the rapid increase of memory and computation requirements hinders their deployment on resource-constrained devices.
MPCViT: Searching for Accurate and Efficient MPC-Friendly Vision Transformer with Heterogeneous Attention
Secure multi-party computation (MPC) enables computation directly on encrypted data and protects both data and model privacy in deep learning inference.
Extremely-Fast, Energy-Efficient Massive MIMO Precoding with Analog RRAM Matrix Computing
Signal processing in wireless communications, such as precoding, detection, and channel estimation, are basically about solving inverse matrix problems, which, however, are slow and inefficient in conventional digital computers, thus requiring a radical paradigm shift to achieve fast, real-time solutions.
CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)
The electronic design automation (EDA) community has been actively exploring machine learning (ML) for very large-scale integrated computer-aided design (VLSI CAD).
Learning optimal multigrid smoothers via neural networks
Multigrid methods are one of the most efficient techniques for solving linear systems arising from Partial Differential Equations (PDEs) and graph Laplacians from machine learning applications.
Generating a Doppelganger Graph: Resembling but Distinct
Deep generative models, since their inception, have become increasingly more capable of generating novel and perceptually realistic signals (e. g., images and sound waves).
DaSGD: Squeezing SGD Parallelization Performance in Distributed Training Using Delayed Averaging
The state-of-the-art deep learning algorithms rely on distributed training systems to tackle the increasing sizes of models and training data sets.
