Efficient Approximate Posit Multipliers for Deep Learning Computation

Project Code :TVMAFE597

Objective

To solve this cost problem, in this paper, posit multipliers with approximate computing features are proposed.

Demo Video

mail-banner
call-banner
contact-banner
Request Video

Related Projects

DSP Core

TVMAFE586Architectural Exploration for Energy-Efficient LMS and NLMS Adaptive Filters VLSI DesignView Details

Communications

TVMAFE572Scalable Low-Cost Sorting Network with Weighted Bit-StreamsView Details
Related Topics
  • Arithmetic Core
  • Communications
  • DSP Core
  • FPGA
  • Core Memories
  • Low Power VLSI
  • Transistor Logic
  • Nano Technology
  • Xilinx ISE
  • FPGA
  • Cadence EDA
  • Xilinx Vivado
  • Matlab Interfacing

Quick Links

Specialization

Our services

Disclaimers

Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.

©2026 TAKEOFF EDU GROUP All Rights Reserved.

Design & developed by YMTSINDIA