Paper-Conference

Efficient FPGA Deployment of Power-of-Two Quantized Networks via Dynamic Hierarchical Base-Exponent Offset Encoding

Adaptive Gradual Quantization with a Custom RISC-V SIMD Accelerator

Neural network quantization is essential for deploying deep learning models on resource-constrained devices, but it presents a critical trade-off: aggressive, low-bit quantization …

A Review of FPGA-Driven LLM Acceleration

This paper provides a brief review of FPGA-based acceleration strategies for large language models (LLMs). As LLMs continue to increase in scale and complexity, efficiently …

FPGA-based real-time image tampering detection system for edge computing

With the proliferation of sophisticated image manipulation techniques, robust and efficient tampering detection has become critically important for multimedia forensics and …

Enhancing Synthesis Efficiency in HLS through LLM-Based Automated Code Correction

The integration of AI-based deep learning and advanced signal processing technologies has become crucial in intelligent edge computing systems. In these applications, HLS …

LightFSA: A Lightweight Financial Sentiment Analysis Model

Financial sentiment analysis involves interpreting information from financial articles, news and social media to understand market trends and guide investment decisions. Numerous …

LHA: Layer-wise Hardware Acceleration of Progressive Quantizing Inference through Partial Reconfiguration for Edge Computing

As the need for real-time, low-power deep learning at the edge increases, efficient hardware acceleration becomes crucial. Traditional edge hardware designs often scale to …

Joint Post-Training Pruning and Power-of-Two Quantization for Efficient Edge Computing

Recent advancements in deep neural networks have created significant challenges for deploying these models on edge devices due to their computational and memory demands. We propose …

A Novel Computing Paradigm for MobileNetV3 using Memristor

The advancement in the field of machine learning is inextricably linked with the concurrent progress in domain-specific hardware accelerators such as GPUs and TPUs. However, the …

KernelVM: Teaching Linux Kernel Programming through a Browser-Based Virtual Machine

Providing students with hands-on experience in kernel programming within a real-world operating system is highly beneficial in an Operating Systems (OS) course for teaching core …