2 citations · 2 across the 4 of their papers we have counts for
6 papers
Characterization and Mitigation of ADC Noise by Reference Tuning in RRAM-Based Compute-In-Memory
Ying-Hao Wei, Zishen Wan, Brian Crafton +2
With the escalating demand for power-efficient neural network architectures, non-volatile compute-in-memory designs have garnered significant attention. However, owing to the natur…
Breaking Barriers: Maximizing Array Utilization for Compute In-Memory Fabrics
Brian Crafton, Samuel Spetalnick, Gauthaman Murali +3
Compute in-memory (CIM) is a promising technique that minimizes data transport, the primary performance bottleneck and energy cost of most data intensive applications. This has fou…
Counting Cards: Exploiting Variance and Data Distributions for Robust Compute In-Memory
Brian Crafton, Samuel Spetalnick, Arijit Raychowdhury
Compute in-memory (CIM) is a promising technique that minimizes data transport, the primary performance bottleneck and energy cost of most data intensive applications. This has fou…
Hardware-aware Pruning of DNNs using LFSR-Generated Pseudo-Random Indices
Foroozan Karimzadeh, Ningyuan Cao, Brian Crafton +2
Deep neural networks (DNNs) have been emerged as the state-of-the-art algorithms in broad range of applications. To reduce the memory foot-print of DNNs, in particular for embedded…
Quantum-statistical transport phenomena in memristive computing architectures
Christopher N. Singh, Brian A. Crafton, Mathew P. West +8
The advent of reliable, nanoscale memristive components is promising for next generation compute-in-memory paradigms, however, the intrinsic variability in these devices has preven…
Direct Feedback Alignment with Sparse Connections for Local Learning
Brian Crafton, Abhinav Parihar, Evan Gebhardt +1
Recent advances in deep neural networks (DNNs) owe their success to training algorithms that use backpropagation and gradient-descent. Backpropagation, while highly effective on vo…