5 papers
ScaleLUT: A Fully-Parallel Configurable LUT-Based Accelerator for Real-Time Multi-Scale Super-Resolution
Boyu Li, Chenchen Ding, Zhilin Ai +7
Real-time super-resolution (SR) remains challenging for edge devices because deep-learning-based methods require substantial multiply-accumulate (MAC) operations, resources, and po…
NOVA-CIM: Noise- and Correlation-Tolerant Stochastic Interfaces for Analog Compute-in-Memory
Jiachen Ren, Wenshuai Yao, Haobo Liu +6
Analog compute-in-memory (CIM) enables energy-efficient model acceleration, but its reliance on ADC-based readout, which directly quantizes noisy column currents, makes inference a…
PatchINR: Patch-Based Implicit Neural Representations for Efficient and Scalable Inference
Jiachen Ren, Wenyong Zhou, Taiqiang Wu +4
Implicit Neural Representation (INR) provides an effective approach for continuous signal modeling, but classical per-pixel inference results in quadratic growth in inference count…
QuadINR: Hardware-Efficient Implicit Neural Representations Through Quadratic Activation
Wenyong Zhou, Boyu Li, Jiachen Ren +4
Implicit Neural Representations (INRs) encode discrete signals continuously while addressing spectral bias through activation functions (AFs). Previous approaches mitigate this bia…
Distribution-Aware Hadamard Quantization for Hardware-Efficient Implicit Neural Representations
Wenyong Zhou, Jiachen Ren, Taiqiang Wu +3
Implicit Neural Representations (INRs) encode discrete signals using Multi-Layer Perceptrons (MLPs) with complex activation functions. While INRs achieve superior performance, they…