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Found 18 papers, 13 papers with code
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
DeepSeek-V3 Technical Report
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token.
DPD-NeuralEngine: A 22-nm 6.6-TOPS/W/mm$^2$ Recurrent Neural Network Accelerator for Wideband Power Amplifier Digital Pre-Distortion
The increasing adoption of Deep Neural Network (DNN)-based Digital Pre-distortion (DPD) in modern communication systems necessitates efficient hardware implementations.
CleanUMamba: A Compact Mamba Network for Speech Denoising using Channel Pruning
This paper presents CleanUMamba, a time-domain neural network architecture designed for real-time causal audio denoising directly applied to raw waveforms.
SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network
SlimSeiz operates in two states: the first stage selects the optimal channel set for seizure prediction using machine learning algorithms, and the second stage employs a lightweight neural network based on convolution and Mamba for prediction.
FACET: Fast and Accurate Event-Based Eye Tracking Using Ellipse Modeling for Extended Reality
Eye tracking is a key technology for gaze-based interactions in Extended Reality (XR), but traditional frame-based systems struggle to meet XR's demands for high accuracy, low latency, and power efficiency.
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks.
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM
This paper introduces DeltaKWS, to the best of our knowledge, the first $\Delta$RNN-enabled fine-grained temporal sparsity-aware KWS IC for voice-controlled devices.
MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifiers
Applied to a 160MHz-BW 1024-QAM OFDM signal from a digital RF PA, MP-DPD gives no performance loss against 32-bit floating-point precision DPDs, while achieving -43. 75 (L)/-45. 27 (R) dBc in Adjacent Channel Power Ratio (ACPR) and -38. 72 dB in Error Vector Magnitude (EVM).
Event-Based Eye Tracking. AIS 2024 Challenge Survey
This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge.
WIKIGENBENCH: Exploring Full-length Wikipedia Generation under Real-World Scenario
It presents significant challenges to generate comprehensive and accurate Wikipedia articles for newly emerging events under a real-world scenario.
Selecting Large Language Model to Fine-tune via Rectified Scaling Law
The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options.
Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer
Epilepsy is a common disease of the nervous system.
3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network
This paper presents a sparse Change-Based Convolutional Long Short-Term Memory (CB-ConvLSTM) model for event-based eye tracking, key for next-generation wearable healthcare technology such as AR/VR headsets.
To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration
The power efficiency of the biological brain outperforms any large-scale deep learning ( DL ) model; thus, neuromorphic computing tries to mimic the brain operations, such as spike-based information processing, to improve the efficiency of DL models.
Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance
Spiking Neural Networks (SNNs) are developed as a promising alternative to Artificial Neural networks (ANNs) due to their more realistic brain-inspired computing models.
Domain Adaptation via Maximizing Surrogate Mutual Information
In the framework, a surrogate joint distribution models the underlying joint distribution of the unlabeled target domain.
