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Found 19 papers, 1 papers with code
A Novel P-bit-based Probabilistic Computing Approach for Solving the 3-D Protein Folding Problem
In the post-Moore era, the need for efficient solutions to non-deterministic polynomial-time (NP) problems is becoming more pressing.
Anda: Unlocking Efficient LLM Inference with a Variable-Length Grouped Activation Data Format
To address these challenges, we investigate the sensitivity of activation precision across various LLM modules and its impact on overall model accuracy.
Jamming Detection and Channel Estimation for Spatially Correlated Beamspace Massive MIMO
For the detected jammer along with users, we propose a two-step minimum mean square error (MMSE) channel estimation using the projected observation vectors.
Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores
When integrated into LLMs, we achieve up to 6. 7\times inference acceleration.
Co-Designing Binarized Transformer and Hardware Accelerator for Efficient End-to-End Edge Deployment
Transformer models have revolutionized AI tasks, but their large size hinders real-world deployment on resource-constrained and latency-critical edge devices.
BETA: Binarized Energy-Efficient Transformer Accelerator at the Edge
Existing binary Transformers are promising in edge deployment due to their compact model size, low computational complexity, and considerable inference accuracy.
Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design
At the algorithm level, a bidirectional weight pruning method, dubbed BDWP, is proposed to leverage the N:M sparsity of weights during both forward and backward passes of DNN training, which can significantly reduce the computational cost while maintaining model accuracy.
A Precision-Scalable RISC-V DNN Processor with On-Device Learning Capability at the Extreme Edge
However, many edge devices struggle to boost inference throughput of various quantized DNNs due to the varying quantization levels, and these devices lack floating-point (FP) support for on-device learning, which prevents them from improving model accuracy while ensuring data privacy.
Caching-at-STARS: the Next Generation Edge Caching
As long-term decision processes, the optimization problems based on independent and coupled phase-shift models of Caching-at-STARS contain both continuous and discrete decision variables, and are suitable for solving with deep reinforcement learning (DRL) algorithm.
NVAutoNet: Fast and Accurate 360$^{\circ}$ 3D Visual Perception For Self Driving
Achieving robust and real-time 3D perception is fundamental for autonomous vehicles.
BEBERT: Efficient and Robust Binary Ensemble BERT
Pre-trained BERT models have achieved impressive accuracy on natural language processing (NLP) tasks.
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators.
An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers
(1) From algorithm perspective, we propose a sparsity inheritance mechanism along with an inherited dynamic pruning (IDP) method to obtain a series of N:M sparse candidate Transformers rapidly.
Joint Scheduling and Throughput Maximization in Self-backhauled Millimeter Wave Cellular Networks
Integrated access and backhaul (IAB) networks have the potential to provide high data rate in both access and backhaul networks by sharing the same spectrum.
PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving
In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning.
Hybrid Precoding in Cooperative Millimeter Wave Networks
Considering joint transmissions and BS silence strategy, we propose hybrid precoding algorithms which minimize the sum power consumption of the base stations (BSs), for both fully- and partially-connected hybrid precoding (FHP and PHP, respectively) schemes, for single-carrier and orthogonal frequency-division multiplexing systems.
Real-Time Panoptic Segmentation from Dense Detections
Panoptic segmentation is a complex full scene parsing task requiring simultaneous instance and semantic segmentation at high resolution.
Training Deep Neural Networks Using Posit Number System
With the increasing size of Deep Neural Network (DNN) models, the high memory space requirements and computational complexity have become an obstacle for efficient DNN implementations.
MUFold-SS: Protein Secondary Structure Prediction Using Deep Inception-Inside-Inception Networks
Results: Here, a very deep neural network, the deep inception-inside-inception networks (Deep3I), is proposed for protein secondary structure prediction and a software tool was implemented using this network.
