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Architectural details of machine learning models are crucial pieces of intellectual property in many applications. Revealing the structure or types of layers in a model can result in a leak of confidential or proprietary information. This issue becomes especially concerning when the machine learning models are executed on accelerators in multi-tenant FPGAs where attackers can easily co-locate sensing circuitry next to the victim's machine learning accelerator. To evaluate such threats, we present the first remote power attack that can extract details of machine learning models executed on an off-the-shelf domain-specific instruction set architecture (ISA) based neural network accelerator implemented in an FPGA. By leveraging a time-to-digital converter (TDC), an attacker can deduce the composition of instruction groups executing on the victim accelerator, and recover parameters of General Matrix Multiplication (GEMM) instructions within a group, all without requiring physical access to the FPGA. With this information, an attacker can then reverse-engineer the structure and layers of machine learning models executing on the accelerator, leading to potential theft of proprietary information.