Year: 2026 | Month: April | Volume: 13 | Issue: 4 | Pages: 363-368
DOI: https://doi.org/10.52403/ijrr.20260436
Review on Deep Network Accelerators Towards Healthcare and Biomedical Applications
Pallavi Anil Talnikar1, Hemangi Ravindra Zunjarrao2
1Lecturer in Automation & Robotics Department, Marathwada Mitra Mandal's Polytechnic, Thergaon, Pune, Maharashtra.
2Lecturer in, Automation & Robotics Department, Marathwada Mitra Mandal's Polytechnic, Thergaon, Pune, Maharashtra.
Corresponding Author: Pallavi Anil Talnikar
ABSTRACT
Recent trends in deep learning (DL) imposed hardware accelerators as the most viable solution for several classes of high-performance computing (HPC) applications such as image classification, computer vision, and speech recognition. This survey summarizes and classifies the most recent advances in designing DL accelerators suitable to reach the performance requirements of HPC applications. In particular, it highlights the most advanced approaches to support deep learning accelerations including not only GPU and TPU-based accelerators but also design-specific hardware accelerators such as FPGA-based and ASIC-based accelerators, Neural Processing Units, open hardware RISC-V-based accelerators and co-processors. The survey also describes accelerators based on emerging memory technologies and computing paradigms, such as 3Dstacked Processor-In-Memory, non-volatile memories (mainly, Resistive RAM and Phase Change Memories) to implement in-memory computing, Neuromorphic Processing Units, and accelerators based on Multi-Chip Modules. The survey classifies the most influential architectures and technologies proposed in recent years, to offer the reader a comprehensive perspective in the rapidly evolving field of deep learning.
Keywords: Deep network accelerators, deep learning, high-performance computing
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