Tempus delivers 607 GOPS at 10.677 W using fixed 16 AIE cores on Versal AI Edge, with 211.2x better platform-aware utility than spatial SOTA ARIES and zero URAM/DSP utilization.
BERT: a review of applications in natural language processing and understanding
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
SAri-RFT applies GRPO-based reinforcement fine-tuning to LVLMs on novel two-term and three-term visual semantic arithmetic tasks, reaching SOTA on the new IRPD dataset and Visual7W-Telling.
SELF-EMO lets LLMs bootstrap better emotion recognition and expression via self-play, data flywheel filtering with smoothed IoU rewards, and SELF-GRPO reinforcement learning, yielding SOTA gains on IEMOCAP, MELD, and EmoryNLP.
A transformer framework for composed vision-language retrieval in skin cancer uses hierarchical query representations and global-local alignment to improve performance over prior methods on the Derm7pt dataset.
A new dataset of 400k visual instructions including negative examples at three semantic levels reduces hallucinations in models like MiniGPT-4 when used for fine-tuning while improving benchmark performance.
Hardware approximations for Softmax and LayerNorm preserve exact normalization guarantees and deliver up to 14x area reduction in 28nm silicon with negligible accuracy loss on GLUE, SQuAD, and perplexity.
LLMs perform well on basic syntactic and semantic bugs in small code but struggle with complex security vulnerabilities and large production codebases.
citing papers explorer
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Tempus: A Temporally Scalable Resource-Invariant GEMM Streaming Framework for Versal AI Edge
Tempus delivers 607 GOPS at 10.677 W using fixed 16 AIE cores on Versal AI Edge, with 211.2x better platform-aware utility than spatial SOTA ARIES and zero URAM/DSP utilization.
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Multi-modal Reasoning with LLMs for Visual Semantic Arithmetic
SAri-RFT applies GRPO-based reinforcement fine-tuning to LVLMs on novel two-term and three-term visual semantic arithmetic tasks, reaching SOTA on the new IRPD dataset and Visual7W-Telling.
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SELF-EMO: Emotional Self-Evolution from Recognition to Consistent Expression
SELF-EMO lets LLMs bootstrap better emotion recognition and expression via self-play, data flywheel filtering with smoothed IoU rewards, and SELF-GRPO reinforcement learning, yielding SOTA gains on IEMOCAP, MELD, and EmoryNLP.
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Composed Vision-Language Retrieval for Skin Cancer Case Search via Joint Alignment of Global and Local Representations
A transformer framework for composed vision-language retrieval in skin cancer uses hierarchical query representations and global-local alignment to improve performance over prior methods on the Derm7pt dataset.
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Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
A new dataset of 400k visual instructions including negative examples at three semantic levels reduces hallucinations in models like MiniGPT-4 when used for fine-tuning while improving benchmark performance.
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Hardware-Efficient Softmax and Layer Normalization with Guaranteed Normalization for Edge Devices
Hardware approximations for Softmax and LayerNorm preserve exact normalization guarantees and deliver up to 14x area reduction in 28nm silicon with negligible accuracy loss on GLUE, SQuAD, and perplexity.
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Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++
LLMs perform well on basic syntactic and semantic bugs in small code but struggle with complex security vulnerabilities and large production codebases.