Home Community Researchers from Tsinghua University Unveil ‘Gemini’: A Latest AI Approach to Boost Performance and Energy Efficiency in Chiplet-Based Deep Neural Network Accelerators

Researchers from Tsinghua University Unveil ‘Gemini’: A Latest AI Approach to Boost Performance and Energy Efficiency in Chiplet-Based Deep Neural Network Accelerators

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Researchers from Tsinghua University Unveil ‘Gemini’: A Latest AI Approach to Boost Performance and Energy Efficiency in Chiplet-Based Deep Neural Network Accelerators

Researchers from multiple universities have addressed the challenge of designing large-scale DNN chiplet accelerators, specializing in optimizing monetary cost (MC), performance, and energy efficiency. The complexity arises from the interplay of assorted parameters, including network-on-chip (NoC) communication, core positions, and different DNN attributes. It’s crucial to explore an enormous design space for effective solutions.

Currently, existing DNN accelerators need assistance in achieving an optimal balance between MC, performance, and energy efficiency. They introduced the architecture and mapping co-exploration framework for DNN chiplet accelerators, Gemini. Gemini employs a novel encoding method to define low-power (LP) spatial mapping schemes, allowing for an exhaustive exploration of hidden optimization opportunities. The framework utilizes a dynamic programming-based graph partition algorithm and a Simulated-Annealing-based (SA-based) approach for optimization.

Gemini’s mapping component uses the SA algorithm with five operators tailored to efficiently explore the LP spatial mapping space. These operators include modifying partition attributes, swapping cores inside computational groups (CG), and adjusting DRAM-related attributes. The framework dynamically optimizes data transmission, intra-core dataflow, and D2D link communication, contributing to enhanced performance and energy efficiency. The evaluation process involves assessing MC, energy consumption, and delay through an Evaluator module.

The architecture aspect of Gemini provides a highly configurable hardware template, enabling precise evaluations for performance, energy, and MC. The proposed framework’s experiments showcase that the explored architecture and mapping scheme outperforms existing state-of-the-art (SOTA) designs like Simba with Tangram mapping. Gemini also achieves significant improvements with only a marginal increase in MC, demonstrating its effectiveness in co-exploring the architecture and mapping space.

In conclusion, the Gemini framework offers a comprehensive solution to the intricate challenges of designing DNN chiplet accelerators. The experiments not only validate Gemini’s effectiveness but in addition make clear the potential advantages of chiplet technology in architecture design. Overall, Gemini stands out as a priceless tool for researchers and practitioners aiming to design high-performance and energy-efficient DNN accelerators.


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Pragati Jhunjhunwala is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Kharagpur. She is a tech enthusiast and has a keen interest within the scope of software and data science applications. She is all the time reading concerning the developments in several field of AI and ML.


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