30 citations · 98 across the 13 of their papers we have counts for
17 papers
XOR-Trellis: Ultra-Low-Complexity Dequantization and Curvature-Aware Hadamard-Free LLM Quantization
Xiaofan Que, Nir Elkayam, Spandan Pyakurel +2
Trellis-coded quantization enables high-dimensional compression of large language model (LLM) weights at ultra-low bit widths without the exponentially large codebooks required by…
Highly Optimized Kernels and Fine-Grained Codebooks for LLM Inference on Arm CPUs
Dibakar Gope, David Mansell, Danny Loh +1
Large language models (LLMs) have transformed the way we think about language understanding and generation, enthralling both researchers and developers. However, deploying LLMs for…
Data-Free Group-Wise Fully Quantized Winograd Convolution via Learnable Scales
Shuokai Pan, Gerti Tuzi, Sudarshan Sreeram +1
Despite the revolutionary breakthroughs of large-scale text-to-image diffusion models for complex vision and downstream tasks, their extremely high computational and storage costs…
Jumping through Local Minima: Quantization in the Loss Landscape of Vision Transformers
Natalia Frumkin, Dibakar Gope, Diana Marculescu
Quantization scale and bit-width are the most important parameters when considering how to quantize a neural network. Prior work focuses on optimizing quantization scales in a glob…
PerfSAGE: Generalized Inference Performance Predictor for Arbitrary Deep Learning Models on Edge Devices
Yuji Chai, Devashree Tripathy, Chuteng Zhou +6
The ability to accurately predict deep neural network (DNN) inference performance metrics, such as latency, power, and memory footprint, for an arbitrary DNN on a target hardware p…
CPT-V: A Contrastive Approach to Post-Training Quantization of Vision Transformers
Natalia Frumkin, Dibakar Gope, Diana Marculescu
When considering post-training quantization, prior work has typically focused on developing a mixed precision scheme or learning the best way to partition a network for quantizatio…
Restructurable Activation Networks
Kartikeya Bhardwaj, James Ward, Caleb Tung +6
Is it possible to restructure the non-linear activation functions in a deep network to create hardware-efficient models? To address this question, we propose a new paradigm called…
Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge
Kartikeya Bhardwaj, Dibakar Gope, James Ward +2
Autonomous systems are highly vulnerable to a variety of adversarial attacks on Deep Neural Networks (DNNs). Training-free model-agnostic defenses have recently gained popularity d…
Collapsible Linear Blocks for Super-Efficient Super Resolution
Kartikeya Bhardwaj, Milos Milosavljevic, Liam O'Neil +6
With the advent of smart devices that support 4K and 8K resolution, Single Image Super Resolution (SISR) has become an important computer vision problem. However, most super resolu…
Rank and run-time aware compression of NLP Applications
Urmish Thakker, Jesse Beu, Dibakar Gope +2
Sequence model based NLP applications can be large. Yet, many applications that benefit from them run on small devices with very limited compute and storage capabilities, while sti…
High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands
Dibakar Gope, Jesse Beu, Matthew Mattina
Matrix multiplications between asymmetric bit-width operands, especially between 8- and 4-bit operands are likely to become a fundamental kernel of many important workloads includi…
The gem5 Simulator: Version 20.0+
Jason Lowe-Power, Abdul Mutaal Ahmad, Ayaz Akram +75
The open-source and community-supported gem5 simulator is one of the most popular tools for computer architecture research. This simulation infrastructure allows researchers to mod…
Ternary MobileNets via Per-Layer Hybrid Filter Banks
Dibakar Gope, Jesse Beu, Urmish Thakker +1
MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years. New applica…
Pushing the limits of RNN Compression
Urmish Thakker, Igor Fedorov, Jesse Beu +4
Recurrent Neural Networks (RNN) can be difficult to deploy on resource constrained devices due to their size. As a result, there is a need for compression techniques that can signi…
Run-Time Efficient RNN Compression for Inference on Edge Devices
Urmish Thakker, Jesse Beu, Dibakar Gope +2
Recurrent neural networks can be large and compute-intensive, yet many applications that benefit from RNNs run on small devices with very limited compute and storage capabilities w…
Compressing RNNs for IoT devices by 15-38x using Kronecker Products
Urmish Thakker, Jesse Beu, Dibakar Gope +4
Recurrent Neural Networks (RNN) can be difficult to deploy on resource constrained devices due to their size.As a result, there is a need for compression techniques that can signif…
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications
Dibakar Gope, Ganesh Dasika, Matthew Mattina
Machine learning-based applications are increasingly prevalent in IoT devices. The power and storage constraints of these devices make it particularly challenging to run modern neu…