Scholar
Jindong Li
Google Scholar ID: jh7XH44AAAAJ
Institute of Automation, Chinese Academy of Sciences
Citations & Impact
Citations
150
H-index
7
i10-index
4
Publications
14
Co-authors
7
list available
Publications
Resume (English only)
Academic Achievements
- First-Author Publications:
- 1. Hummingbird: A Smaller and Faster Large Language Model Accelerator on Embedded FPGA
- 2. Pushing up to the Limit of Memory Bandwidth and Capacity Utilization for Efficient LLM Decoding on Embedded FPGA
- 3. Revealing Untapped DSP Optimization Potentials for FPGA-Based Systolic Matrix Engines
- 4. Firefly v2: Advancing hardware support for high-performance spiking neural network with a spatiotemporal fpga accelerator
- 5. Firefly: A high-throughput hardware accelerator for spiking neural networks with efficient dsp and memory optimization
- Co-Authored Publications:
- 1. FireFly-S: Exploiting Dual-Side Sparsity for Spiking Neural Networks Acceleration With Reconfigurable Spatial Architecture
- 2. SpikePack: Enhanced Information Flow in Spiking Neural Networks with High Hardware Compatibility
- 3. Are Conventional SNNs Really Efficient? A Perspective from Network Quantization
- 4. Implementation of CNN Heterogeneous Scheme Based on Domestic FPGA with RISC-V Soft Core CPU
- 5. Hardware Resource and Computational Density Efficient CNN Accelerator Design Based on FPGA
- Preprints:
- 1. FireFly-T: High-Throughput Sparsity Exploitation for Spiking Transformer Acceleration with Dual-Engine Overlay Architecture
Research Experience
- Working at the Institute of Automation, Chinese Academy of Sciences.
Education
- Currently pursuing a Ph.D. at the Institute of Automation, Chinese Academy of Sciences, under the supervision of Professor Yi Zeng.
Background
- Research Interests: Hardware accelerators for deep learning models, specifically Large Language Models (LLMs), Convolutional Neural Networks (CNNs), and Spiking Neural Networks (SNNs). Brief Introduction: A third-year Ph.D. student at the Institute of Automation, Chinese Academy of Sciences, supervised by Professor Yi Zeng.
