5 citations · 9 across the 6 of their papers we have counts for
6 papers
Mem-Rec: Memory Efficient Recommendation System using Alternative Representation
Gopi Krishna Jha, Anthony Thomas, Nilesh Jain +3
Deep learning-based recommendation systems (e.g., DLRMs) are widely used AI models to provide high-quality personalized recommendations. Training data used for modern recommendatio…
Streaming Encoding Algorithms for Scalable Hyperdimensional Computing
Anthony Thomas, Behnam Khaleghi, Gopi Krishna Jha +5
Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience. HDC represents data as high-dimensional, low-precisio…
EZNAS: Evolving Zero Cost Proxies For Neural Architecture Scoring
Yash Akhauri, J. Pablo Munoz, Nilesh Jain +1
Neural Architecture Search (NAS) has significantly improved productivity in the design and deployment of neural networks (NN). As NAS typically evaluates multiple models by trainin…
Enabling NAS with Automated Super-Network Generation
J. Pablo Muñoz, Nikolay Lyalyushkin, Yash Akhauri +3
Recent Neural Architecture Search (NAS) solutions have produced impressive results training super-networks and then deriving subnetworks, a.k.a. child models that outperform expert…
RHNAS: Realizable Hardware and Neural Architecture Search
Yash Akhauri, Adithya Niranjan, J. Pablo Muñoz +6
The rapidly evolving field of Artificial Intelligence necessitates automated approaches to co-design neural network architecture and neural accelerators to maximize system efficien…
Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization
Santiago Miret, Vui Seng Chua, Mattias Marder +3
Mixed-precision quantization is a powerful tool to enable memory and compute savings of neural network workloads by deploying different sets of bit-width precisions on separate com…