Optimal deployment of deep neural networks (DNNs) on state-of-the-art Systems-on-Chips (SoCs) is crucial for tiny machine learning (TinyML) at the edge. The complexity of these SoCs makes deployment non-trivial, as they typically contain multiple heterogeneous compute cores with limited, programmer-managed memory to optimize latency and energy efficiency. We propose HTVM - a compiler that merges TVM with DORY to maximize the utilization of heterogeneous accelerators and minimize data movements. HTVM allows deploying the MLPerfT Tiny suite on DIANA, an SoC with a RISC-V CPU, and digital and analog compute-in-memory AI accelerators, at 120x improved performance over plain TVM deployment.
HTVM: Efficient Neural Network Deployment On Heterogeneous TinyML Platforms / Van Delm, Josse; Vandersteegen, Maarten; Burrello, Alessio; Sarda, Giuseppe Maria; Conti, Francesco; Pagliari, Daniele Jahier; Benini, Luca; Verhelst, Marian. - (2023). (Intervento presentato al convegno Design Automation Conference (DAC) 2023 tenutosi a San Francisco, CA (USA) nel 09-13 July 2023) [10.1109/dac56929.2023.10247664].
HTVM: Efficient Neural Network Deployment On Heterogeneous TinyML Platforms
Burrello, Alessio;Pagliari, Daniele Jahier;
2023
Abstract
Optimal deployment of deep neural networks (DNNs) on state-of-the-art Systems-on-Chips (SoCs) is crucial for tiny machine learning (TinyML) at the edge. The complexity of these SoCs makes deployment non-trivial, as they typically contain multiple heterogeneous compute cores with limited, programmer-managed memory to optimize latency and energy efficiency. We propose HTVM - a compiler that merges TVM with DORY to maximize the utilization of heterogeneous accelerators and minimize data movements. HTVM allows deploying the MLPerfT Tiny suite on DIANA, an SoC with a RISC-V CPU, and digital and analog compute-in-memory AI accelerators, at 120x improved performance over plain TVM deployment.File | Dimensione | Formato | |
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HTVM_Efficient_Neural_Network_Deployment_On_Heterogeneous_TinyML_Platforms.pdf
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https://hdl.handle.net/11583/2991620