4 citations · 4 across the 6 of their papers we have counts for
6 papers
ADS-IMC: Accelerating Data Sorting with In-Memory Computation
Narendra Singh Dhakad, Santosh Kumar Vishvakarma
Sorting is a fundamental operation across numerous computational domains. Traditionally, this process involves transferring data from main memory to a processing unit for sorting,…
SRAM Based Digital Custom Compute Engine for Improved Area Efficiency of AI Hardware
Narendra Singh Dhakad, Santosh Kumar Vishvakarma
This paper presents a novel architecture utilizing a 10T SRAM cell for XNOR-based in-memory computing, aimed at mitigating the extensive routing challenges typically encountered in…
First Demonstration of 28 nm Fabricated FeFET-Based Nonvolatile 6T SRAM
Albi Mema, Simon Thomann, Narendra Singh Dhakad +1
With the staggering increase of edge compute applications like Internet-of-Things (IoT) and artificial intelligence (AI), the demand for fast, energy-efficient on-chip memory is gr…
Res-DPU: Resource-shared Digital Processing-in-memory Unit for Edge-AI Workloads
Mukul Lokhande, Narendra Singh Dhakad, Seema Chouhan +2
Processing-in-memory (PIM) has emerged as the go to solution for addressing the von Neumann bottleneck in edge AI accelerators. However, state-of-the-art (SoTA) digital PIM approac…
SHA-CNN: Scalable Hierarchical Aware Convolutional Neural Network for Edge AI
Narendra Singh Dhakad, Yuvnish Malhotra, Santosh Kumar Vishvakarma +1
This paper introduces a Scalable Hierarchical Aware Convolutional Neural Network (SHA-CNN) model architecture for Edge AI applications. The proposed hierarchical CNN model is metic…
Configurable Multi-Port Memory Architecture for High-Speed Data Communication
Narendra Singh Dhakad, Santosh Kumar Vishvakarma
Memory management is necessary with the increasing number of multi-connected AI devices and data bandwidth issues. For this purpose, high-speed multi-port memory is used. The tradi…