5 citations · 7 across the 8 of their papers we have counts for
9 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…
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…
Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay
Kuluhan Binici, Shivam Aggarwal, Nam Trung Pham +2
Data-Free Knowledge Distillation (KD) allows knowledge transfer from a trained neural network (teacher) to a more compact one (student) in the absence of original training data. Ex…
Synthetic Video Generation for Robust Hand Gesture Recognition in Augmented Reality Applications
Varun Jain, Shivam Aggarwal, Suril Mehta +1
Hand gestures are a natural means of interaction in Augmented Reality and Virtual Reality (AR/VR) applications. Recently, there has been an increased focus on removing the dependen…