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Non-volatile memory (NVM) technologies such as spin-transfer torque magnetic random access memory (STT-MRAM) and spin-orbit torque magnetic random access memory (SOT-MRAM) have significant advantages compared to conventional SRAM due to their non-volatility, higher cell density, and scalability features. While previous work has investigated several architectural implications of NVM for generic applications, in this work we present DeepNVM, a framework to characterize, model, and analyze NVM-based caches in GPU architectures for deep learning (DL) applications by combining technologyspecific circuit-level models and the actual memory behavior of various DL workloads. We present both iso-capacity and isoarea performance and energy analysis for systems whose lastlevel caches rely on conventional SRAM and emerging STT-MRAM and SOT-MRAM technologies. In the iso-capacity case, STT-MRAM and SOT-MRAM provide up to 4.2× and 5× energy-delay product (EDP) reduction and 2.4× and 3× area reduction compared to conventional SRAM, respectively. Under iso-area assumptions, STT-MRAM and SOT-MRAM provide 2.3× EDP reduction on average across all workloads when compared to SRAM. Our comprehensive cross-layer framework is demonstrated on STT-/SOT-MRAM technologies and can be used for the characterization, modeling, and analysis of any NVM technology for last-level caches in GPU platforms for deep learning applications.