83 citations · 94 across the 18 of their papers we have counts for
4 papers · 1 filter
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence
Priyesh Shukla, Shamma Nasrin, Nastaran Darabi +2
We propose MC-CIM, a compute-in-memory (CIM) framework for robust, yet low power, Bayesian edge intelligence. Deep neural networks (DNN) with deterministic weights cannot express t…
ENOS: Energy-Aware Network Operator Search for Hybrid Digital and Compute-in-Memory DNN Accelerators
Shamma Nasrin, Ahish Shylendra, Yuti Kadakia +4
This work proposes a novel Energy-Aware Network Operator Search (ENOS) approach to address the energy-accuracy trade-offs of a deep neural network (DNN) accelerator. In recent year…
Probabilistic Localization of Insect-Scale Drones on Floating-Gate Inverter Arrays
Priyesh Shukla, Ankith Muralidhar, Nick Iliev +3
We propose a novel compute-in-memory (CIM)-based ultra-low-power framework for probabilistic localization of insect-scale drones. The conventional probabilistic localization approa…
MF-Net: Compute-In-Memory SRAM for Multibit Precision Inference using Memory-immersed Data Conversion and Multiplication-free Operators
Shamma Nasrin, Diaa Badawi, Ahmet Enis Cetin +2
We propose a co-design approach for compute-in-memory inference for deep neural networks (DNN). We use multiplication-free function approximators based on ell_1 norm along with a c…