Machine Learning Lab

This work proposes an outlier-aware quantization method that preserves rare but important weight activations during compression. By dynamically assigning higher precision to outlier weights, it minimizes performance loss in low-bit settings. The technique significantly reduces LLM size while maintaining accuracy, enabling deployment on edge and memory-constrained systems. TDQ introduces a step-aware quantization strategy for diffusion models, dynamically adjusting precision across denoising stages. Early, noise-heavy steps use higher precision for stability, while later steps adopt lower precision for efficiency. This temporal adaptation significantly reduces memory and compute costs without compromising image fidelity. This work proposes a hardware–algorithm co-design approach that extends DRAM-based Processing-in-Memory (PIM) to efficiently handle group-wise quantized LLM operations. By integrating quantization-aware dataflow directly into memory, it reduces data movement bottlenecks and improves throughput. The result is a cost-effective system that accelerates inference for large-scale models on memory-bound platforms. Robust and Reliable AI research aims to ensure that AI systems not only perform well under ideal conditions but also maintain their integrity and trustworthiness in the wild. As AI is deployed in high-stakes domains (from medical diagnosis to autonomous driving), it must be resilient to noisy inputs, adversarial perturbations, or shifts in context, and it should produce outputs that users can trust. Our work in this category ranges from mitigating issues like hallucinations in language models to techniques for stable learning across domains. Ultimately, this research strives to make AI behavior more predictable, transparent, and aligned with human exp