1 citations · 1 across the 7 of their papers we have counts for
8 papers
Efficient Text-to-Image Generation: An Adaptive Step Schedule Controller for Diffusion Models
Kuluhan Binici, Cihan Acar, Shivam Aggarwal +2
Text-to-image diffusion models often use a fixed number of denoising steps, balancing time costs and image quality. However, the optimal number of steps depends on the complexity o…
HALO: Hardware-aware quantization with low critical-path-delay weights for LLM acceleration
Rohan Juneja, Shivam Aggarwal, Safeen Huda +2
Quantization is critical for efficiently deploying large language models (LLMs). Yet conventional methods remain hardware-agnostic, limited to bit-width constraints, and do not acc…
TerEffic: Highly Efficient Ternary LLM Inference on FPGA
Chenyang Yin, Zhenyu Bai, Pranav Venkatram +3
Deploying Large Language Models (LLMs) efficiently on edge devices is often constrained by limited memory capacity and high power consumption. Low-bit quantization methods, particu…
DAOP: Data-Aware Offloading and Predictive Pre-Calculation for Efficient MoE Inference
Yujie Zhang, Shivam Aggarwal, Tulika Mitra
Mixture-of-Experts (MoE) models, though highly effective for various machine learning tasks, face significant deployment challenges on memory-constrained devices. While GPUs offer…
Condensed Data Expansion Using Model Inversion for Knowledge Distillation
Kuluhan Binici, Shivam Aggarwal, Cihan Acar +4
Condensed datasets offer a compact representation of larger datasets, but training models directly on them or using them to enhance model performance through knowledge distillation…
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student Architectures
Kuluhan Binici, Weiming Wu, Tulika Mitra
Knowledge distillation (KD) is a model compression method that entails training a compact student model to emulate the performance of a more complex teacher model. However, the arc…
CRISP: Hybrid Structured Sparsity for Class-aware Model Pruning
Shivam Aggarwal, Kuluhan Binici, Tulika Mitra
Machine learning pipelines for classification tasks often train a universal model to achieve accuracy across a broad range of classes. However, a typical user encounters only a lim…
Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs
Shivam Aggarwal, Hans Jakob Damsgaard, Alessandro Pappalardo +4
Post-training quantization (PTQ) is a powerful technique for model compression, reducing the numerical precision in neural networks without additional training overhead. Recent wor…