Akshat Ramachandran

I am a first-year Ph.D student in Electrical and Computer Engineering (ECE) at the Synergy Lab in the Georgia Institute of Technology (Georgia Tech) advised by Prof. Tushar Krishna.

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The central focus of my research is in the design and development of efficient and high performance algorithms, architectures and systems for accelerating emergent deep learning applications (computer vision and NLP). My research breaks down traditional barriers existing between different computing elements and adopts an interdisciplinary approach spanning the entire computing stack. To realise my vision of developing the next generation of computing systems, my interests and technical work spans a wide gamut and lies at the intersection of computer architecture, VLSI, computer arithmetic and deep learning.

• Algorithms:
  
  • Quantization: [DAC'24]
    
  • 3D Vision: [ICIP'23] [ICONAT'22]
  • Geoscience and Remote Sensing: [All Earth'23] [SPIN'23]
    

• Computer Arithmetic:
  
  • Posits: [DAC'24] [DSD'22]

• Architecture:
  
  • Domain-Specific Architecture for Computer Vision: [DAC'24] [DSD'22]

We present Logarithmic Posits (LP), a novel adaptive data type for Deep Neural Network (DNN) quantization, offering significant efficiency and accuracy improvements. Our approach, LP Quantization (LPQ), optimizes DNN parameters using a genetic algorithm, closely preserving model accuracy with a specialized objective. The resulting LP accelerator architecture (LPA) doubles performance per area and improves energy efficiency by 2.2x over traditional quantization methods, with minimal accuracy loss.

• Conference Reviewer: CoNGA'24@SC Asia 24.

• Sports: I enjoy basketball, swimming, tennis and table tennis.
• Music: I am a trained veena and violin player. [YouTube]

Last Update: Apr. 2024

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