7 citations · 10 across the 8 of their papers we have counts for
8 papers
MicroScopiQ: Accelerating Foundational Models through Outlier-Aware Microscaling Quantization
Akshat Ramachandran, Souvik Kundu, Tushar Krishna
Quantization of foundational models (FMs) is significantly more challenging than traditional DNNs due to the emergence of large magnitude values called outliers. Existing outlier-a…
CLAMP-ViT: Contrastive Data-Free Learning for Adaptive Post-Training Quantization of ViTs
Akshat Ramachandran, Souvik Kundu, Tushar Krishna
We present CLAMP-ViT, a data-free post-training quantization method for vision transformers (ViTs). We identify the limitations of recent techniques, notably their inability to lev…
SDQ: Sparse Decomposed Quantization for LLM Inference
Geonhwa Jeong, Po-An Tsai, Stephen W. Keckler +1
Recently, large language models (LLMs) have shown surprising performance in task-specific workloads as well as general tasks with the given prompts. However, to achieve unprecedent…
H3DFact: Heterogeneous 3D Integrated CIM for Factorization with Holographic Perceptual Representations
Zishen Wan, Che-Kai Liu, Mohamed Ibrahim +4
Disentangling attributes of various sensory signals is central to human-like perception and reasoning and a critical task for higher-order cognitive and neuro-symbolic AI systems.…
Algorithm-Hardware Co-Design of Distribution-Aware Logarithmic-Posit Encodings for Efficient DNN Inference
Akshat Ramachandran, Zishen Wan, Geonhwa Jeong +2
Traditional Deep Neural Network (DNN) quantization methods using integer, fixed-point, or floating-point data types struggle to capture diverse DNN parameter distributions at low p…
Enabling Unstructured Sparse Acceleration on Structured Sparse Accelerators
Geonhwa Jeong, Po-An Tsai, Abhimanyu R. Bambhaniya +2
Exploiting sparsity in deep neural networks (DNNs) has been a promising area for meeting the growing computation requirements. To minimize the overhead of sparse acceleration, hard…
GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM
Hao Kang, Qingru Zhang, Souvik Kundu +4
Key-value (KV) caching has become the de-facto to accelerate generation speed for large language models (LLMs) inference. However, the growing cache demand with increasing sequence…
Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI
Zishen Wan, Che-Kai Liu, Hanchen Yang +7
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, have significantly impacted various aspects of our lives. However, the curren…