Changes

At its core, ZCU104 integrates an array of processing elements including a quad-core ARM Cortex-A53 Application Processing Unit (APU)<ref>Microprocessor that combines both traditional CPU and GPU cores onto a single chip.</ref>, which is based on an ARM64 architecture, and a dual-core ARM Cortex-R5 Real-Time Processing Unit (RPU)<ref>Dedicated hardware component or processor designed to execute tasks or operations with strict timing constraints. RPUs are commonly employed in systems that require immediate and predictable responses, such as embedded systems, robotics, and real-time control applications.</ref>. This processing power allows for efficient and parallel execution of complex [[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1|ML inference algorithms]], making it an ideal choice for applications that demand real-time processing capabilities. It also features a Mali-400 MP2 Graphics Processing Unit, 16nm FinFET+ Programmable Logic, and 2 GB of DDR4 RAM. The peculiarity of this SoC that distinguishes it from competitors’ products is the fact that it integrates a Field Programmable Gate Array (FPGA)<ref>Re-configurable hardware device that allows users to implement custom digital circuits and functions by programming its internal logic gates and interconnections.</ref>, which is strictly coupled to the ARM processors. The ZCU104 boasts an array of high-speed interfaces, such as Gigabit Ethernet, USB 3.0, and DisplayPort, enabling seamless connectivity with external devices and peripherals. From the other side, at the heart of the SBC ORCA lies the NXP i.MX8M Plus SoC featuring a quad-core Arm Cortex-A53 CPU, which is based on ARM64 architecture, and a powerful Neural Processing Unit (NPU). The inclusion of the NPU enhances the platform’s ability to accelerate ML workloads, providing significant speed-up and power efficiency for [[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1|neural network-based inference algorithms]]. The SBC ORCA is equipped with ample memory resources, including 6 GB of LPDDR4 RAM, to accommodate large datasets and complex ML models. Even the SBC ORCA offers a variety of high-speed interfaces, such as Gigabit Ethernet, USB, and HDMI, which enable seamless connectivity with external devices and peripherals. The research environment deployed on the two embedded devices was meticulously constructed, incorporating Python’s virtual environment (<code>python-venv</code>). The environment utilizes <code>python-venv</code>, version 3.10.6, for the Xilinx Zynq UltraScale+ MPSoC ZCU104 device and version 3.9.1 for the DAVE Embedded Systems SBC ORCA device, ensuring precise version control and compatibility tailored to each device’s capabilities. To ensure uniformity and maintainability across different environments, the same <code>requirements.txt</code> file employed in the VM environment was seamlessly integrated into the Python virtual environments of both embedded devices. By employing this unified approach, it was guaranteed that each virtual environment within the embedded devices closely mirrors the environment within the VM, thereby enhancing reproducibility and streamlining research tasks and experiments across diverse hardware platforms. Even in this case, the role of the server was performed by the notebook machine described previously. The following table illustrates the characteristics of the machines used for the "embedded environment".

! Machine

! Component

| ARM64

| -

==== ML framework ====