QUARK: Quantization-Enabled Circuit Sharing for Transformer Acceleration by Exploiting Common Patterns in Nonlinear Operations
📰 ArXiv cs.AI
QUARK is a quantization-enabled FPGA acceleration framework for Transformer models that exploits common patterns in nonlinear operations to reduce inference latency
Action Steps
- Identify common patterns in nonlinear operations of Transformer models
- Apply quantization techniques to reduce computational complexity
- Implement QUARK framework on FPGA hardware to accelerate inference
- Evaluate and fine-tune QUARK for specific CV and NLP tasks
Who Needs to Know This
AI engineers and researchers working on optimizing Transformer models for computer vision and natural language processing tasks can benefit from QUARK, as it provides a novel approach to accelerating nonlinear operations
Key Insight
💡 Exploiting common patterns in nonlinear operations can significantly reduce inference latency in Transformer models
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💡 QUARK: Accelerate Transformer models with quantization-enabled FPGA framework
Key Takeaways
QUARK is a quantization-enabled FPGA acceleration framework for Transformer models that exploits common patterns in nonlinear operations to reduce inference latency
Full Article
Title: QUARK: Quantization-Enabled Circuit Sharing for Transformer Acceleration by Exploiting Common Patterns in Nonlinear Operations
Abstract:
arXiv:2511.06767v2 Announce Type: replace-cross Abstract: Transformer-based models have revolutionized computer vision (CV) and natural language processing (NLP) by achieving state-of-the-art performance across a range of benchmarks. However, nonlinear operations in models significantly contribute to inference latency, presenting unique challenges for efficient hardware acceleration. To this end, we propose QUARK, a quantization-enabled FPGA acceleration framework that leverages common patterns
Abstract:
arXiv:2511.06767v2 Announce Type: replace-cross Abstract: Transformer-based models have revolutionized computer vision (CV) and natural language processing (NLP) by achieving state-of-the-art performance across a range of benchmarks. However, nonlinear operations in models significantly contribute to inference latency, presenting unique challenges for efficient hardware acceleration. To this end, we propose QUARK, a quantization-enabled FPGA acceleration framework that leverages common patterns
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