DNN-Based VLSI Architecture Development
Budget: ₹1,500 – ₹12,500 INR
I am at the initial stages of a project that aims at creating a
Deep Neural Network based Low power VLSI (Very Large-Scale Integration) architecture that is designed to classify Atrial Fibrillation for wearable devices. This ambitious project requires specialised expertise and precise execution.
Key Tasks:
- The design of a Low-Power, DNN-Based VLSI architecture, which is yet to be initiated.
- Create a Full Custom VLSI that is suited for classifying Atrial Fibrillation.
Ideal Skills and Experience:
-Verilog.
-FPGA.
- Relevant experience and extensive knowledge of designing Full Custom VLSI architectures.
- Proven expertise in VLSI architectures and their relation to wearable device technology.
- Firm understanding of Atrial Fibrillation classification.
- Familiarity with power optimisation in VLSI designs would be an advantage, although not a requirement.
Candidates who can apply their deep understanding of neural networks to the design and optimisation of this architecture, towards practical solutions for Atrial Fibrillation applications will stand out. The primary objective here is to deliver a design that edges closer to an efficient and effective solution to present day challenges.
Deep Neural Network based Low power VLSI (Very Large-Scale Integration) architecture that is designed to classify Atrial Fibrillation for wearable devices. This ambitious project requires specialised expertise and precise execution.
Key Tasks:
- The design of a Low-Power, DNN-Based VLSI architecture, which is yet to be initiated.
- Create a Full Custom VLSI that is suited for classifying Atrial Fibrillation.
Ideal Skills and Experience:
-Verilog.
-FPGA.
- Relevant experience and extensive knowledge of designing Full Custom VLSI architectures.
- Proven expertise in VLSI architectures and their relation to wearable device technology.
- Firm understanding of Atrial Fibrillation classification.
- Familiarity with power optimisation in VLSI designs would be an advantage, although not a requirement.
Candidates who can apply their deep understanding of neural networks to the design and optimisation of this architecture, towards practical solutions for Atrial Fibrillation applications will stand out. The primary objective here is to deliver a design that edges closer to an efficient and effective solution to present day challenges.