440 citations · 797 across the 15 of their papers we have counts for
17 papers
Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aaron Blakeman +572
We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 t…
Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aakshita Chandiramani +544
We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemo…
Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation
Zhuolin Yang, Zihan Liu, Yang Chen +14
We introduce Nemotron-Cascade 2, an open 30B MoE model with 3B activated parameters that delivers best-in-class reasoning and strong agentic capabilities. Despite its compact size,…
Hidden costs for inference with deep network on embedded system devices
Chankyu Lee, Woohyun Choi, Sangwook Park
This study evaluates the inference performance of various deep learning models under an embedded system environment. In previous works, Multiply-Accumulate operation is typically u…
Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models
Boxin Wang, Chankyu Lee, Nayeon Lee +9
Building general-purpose reasoning models with reinforcement learning (RL) entails substantial cross-domain heterogeneity, including large variation in inference-time response leng…
AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy
Zihan Liu, Zhuolin Yang, Yang Chen +4
In this work, we investigate the synergy between supervised fine-tuning (SFT) and reinforcement learning (RL) in developing strong reasoning models. We begin by curating the SFT tr…
AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning
Yang Chen, Zhuolin Yang, Zihan Liu +5
Despite recent progress in large-scale reinforcement learning (RL) for reasoning, the training recipe for building high-performing reasoning models remains elusive. Key implementat…
MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs
Sheng-Chieh Lin, Chankyu Lee, Mohammad Shoeybi +3
State-of-the-art retrieval models typically address a straightforward search scenario, in which retrieval tasks are fixed (e.g., finding a passage to answer a specific question) an…
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models
Chankyu Lee, Rajarshi Roy, Mengyao Xu +4
Decoder-only LLM-based embedding models are beginning to outperform BERT or T5-based embedding models in general-purpose text embedding tasks, including dense vector-based retrieva…
ChatQA: Surpassing GPT-4 on Conversational QA and RAG
Zihan Liu, Wei Ping, Rajarshi Roy +4
In this work, we introduce ChatQA, a suite of models that outperform GPT-4 on retrieval-augmented generation (RAG) and conversational question answering (QA). To enhance generation…
Fusion-FlowNet: Energy-Efficient Optical Flow Estimation using Sensor Fusion and Deep Fused Spiking-Analog Network Architectures
Chankyu Lee, Adarsh Kumar Kosta, Kaushik Roy
Standard frame-based cameras that sample light intensity frames are heavily impacted by motion blur for high-speed motion and fail to perceive scene accurately when the dynamic ran…
Towards Understanding the Effect of Leak in Spiking Neural Networks
Sayeed Shafayet Chowdhury, Chankyu Lee, Kaushik Roy
Spiking Neural Networks (SNNs) are being explored to emulate the astounding capabilities of human brain that can learn and compute functions robustly and efficiently with noisy spi…
Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks
Chankyu Lee, Adarsh Kumar Kosta, Alex Zihao Zhu +3
Event-based cameras display great potential for a variety of tasks such as high-speed motion detection and navigation in low-light environments where conventional frame-based camer…
X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks
Amogh Agrawal, Chankyu Lee, Kaushik Roy
There is widespread interest in emerging technologies, especially resistive crossbars for accelerating Deep Neural Networks (DNNs). Resistive crossbars offer a highly-parallel and…
A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks
Saima Sharmin, Priyadarshini Panda, Syed Shakib Sarwar +3
In this era of machine learning models, their functionality is being threatened by adversarial attacks. In the face of this struggle for making artificial neural networks robust, f…
Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures
Chankyu Lee, Syed Shakib Sarwar, Priyadarshini Panda +2
Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing paradigm. However, the typical shallow SNN architectures have limited capacity for expressing c…
X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories
Amogh Agrawal, Akhilesh Jaiswal, Chankyu Lee +1
Silicon-based Static Random Access Memories (SRAM) and digital Boolean logic have been the workhorse of the state-of-art computing platforms. Despite tremendous strides in scaling…