References & Citations
Computer Science > Emerging Technologies
Title: Architecture-Level Modeling of Photonic Deep Neural Network Accelerators
(Submitted on 12 May 2024 (v1), last revised 14 May 2024 (this version, v2))
Abstract: Photonics is a promising technology to accelerate Deep Neural Networks as it can use optical interconnects to reduce data movement energy and it enables low-energy, high-throughput optical-analog computations.
To realize these benefits in a full system (accelerator + DRAM), designers must ensure that the benefits of using the electrical, optical, analog, and digital domains exceed the costs of converting data between domains. Designers must also consider system-level energy costs such as data fetch from DRAM. Converting data and accessing DRAM can consume significant energy, so to evaluate and explore the photonic system space, there is a need for a tool that can model these full-system considerations.
In this work, we show that similarities between Compute-in-Memory (CiM) and photonics let us use CiM system modeling tools to accurately model photonics systems. Bringing modeling tools to photonics enables evaluation of photonic research in a full-system context, rapid design space exploration, co-design, and comparison between systems.
Using our open-source model, we show that cross-domain conversion and DRAM can consume a significant portion of photonic system energy. We then demonstrate optimizations that reduce conversions and DRAM accesses to improve photonic system energy efficiency by up to 3x.
Submission history
From: Tanner Andrulis [view email][v1] Sun, 12 May 2024 12:20:26 GMT (867kb,D)
[v2] Tue, 14 May 2024 13:08:48 GMT (867kb,D)
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