FPGA Insights has engaged in an exclusive interview with Abdur Rehman Khalid, an FPGA Design Engineer at EPTeck
Q1 – Can you provide an overview of your organization and the services/products it offers?
I’ve been working for 2 years as an Embedded FPGA Engineer at EPTeck GmbH. We’re working on cutting-edge technologies for Machine learning, DSP, and image processing applications. Our expertise lies in Embedded Linux + FPGA technologies.
Q2 – Can you explain the benefits of using FPGAs over other types of processors?
There are a lot of benefits of FPGA over CPUs.
Customization:
This feature provides the ability to customize the hardware even after deploying into the market. It just needed to change the bitstream stored in the non-volatile memory storage. SRAM FPGAs have the ability to reprogram the FPGA hardware as many times as possible, unlike anti-fuse based FPGAs. Xilinx and Altera use the SRAM-based fpga technology.
Parallel Processing and Low Latency:
FPGAs excel at parallel processing due to their highly configurable hardware architecture. Unlike traditional processors that execute instructions sequentially using arithmetic logic units (ALUs), FPGAs can perform multiple tasks simultaneously. This parallelism translates to significantly lower latency, making FPGAs ideal for applications that require rapid and predictable response times. Tasks can be pipelined at the gate level, allowing for real-time processing of data with minimal delays.
Power Efficiency:
FPGAs are power-efficient due to their ability to optimize workloads for specific tasks. Unlike CPUs, which are designed for general-purpose computing, FPGAs are configured to perform only the necessary computations. FPGAs can perform computations in fewer clock cycles compared to CPUs. This results in quicker task completion and lower power consumption per operation.
Q3 – What are the most significant trends observed in the FPGA industry over the past year? How will these trends shape the industry’s future?
Hardware Acceleration with Vitis Kernels:
Xilinx has introduced groundbreaking hardware acceleration technology, known as Vitis Kernels, in the FPGA industry. This technology leverages an OpenCL-based software environment, making it easier for developers to harness the power of FPGAs to accelerate specific tasks. Developers can create multiple Vitis RTL kernels and deploy them in parallel in the programmable logic area of FPGAs. The entire process is controlled through Vitis OpenCL APIs, simplifying FPGA programming and allowing for faster development of FPGA-accelerated applications.
Data Center Acceleration Cards:
FPGA manufacturers like Xilinx are focusing on creating specialized data center acceleration cards that utilize their FPGA technology. These cards are designed to provide high-performance acceleration for a wide range of workloads, making them suitable for data centers and cloud computing environments. They typically connect to host servers via PCIe interfaces, allowing for seamless integration into existing server infrastructure.
For instance, Xilinx’s Alveo U250 data center card is a notable example. It offers substantial on-chip and off-chip memory features:
Off-Chip Memory: With 54GB of PL DDR4 off-chip memory, the Alveo U250 provides high memory bandwidth (77GB/s in total). This extensive memory capacity is essential for handling large datasets and complex computations efficiently.
On-Chip SRAM Memory: The card also boasts 54MB of on-chip SRAM memory, delivering exceptional memory bandwidth (38TB/s in total). On-chip SRAM is known for its low-latency access, making it ideal for tasks that require rapid data processing.
Q4 – How do you see FPGA development evolving to meet the demands of modern applications and complex workloads?
Traditionally, FPGA development required expertise in hardware description languages like Verilog, VHDL, and SystemC, resulting in time-consuming and costly projects. Xilinx addresses this challenge with its Vitis HLS (High-Level Synthesis) tool, allowing developers to efficiently convert C-level logic into RTL (Register-Transfer Level) designs, significantly cutting development time and costs. This tool is particularly well-suited for DSP applications, eliminating the complexity of IP integration with Axi4 interfaces. Xilinx’s HLS pragmas streamline Axi4 Memory-Mapped (MM) integration, enhancing FPGA development accessibility and efficiency. Furthermore, Xilinx offers extensive vision libraries for image and video applications, enabling rapid and cost-effective development.
Q5 – What are key drivers behind the increasing adoption of FPGAs in various applications and industries?
FPGAs are adopting more encryption features in their devices that make it compatible with security applications like defense and space. Modern FPGAs, like Xilinx 7 series devices, incorporate advanced bitstream encryption for protecting design logic against unauthorized access and reverse engineering. This encryption, managed through Vivado software, utilizes on-chip AES decryption logic dedicated exclusively to secure bitstream loading. This specialized AES unit ensures that the FPGA’s proprietary logic remains inaccessible to unauthorized users, enhancing design security and safeguarding intellectual property.
Q6 – The role of FPGAs in accelerating AI applications and advancements expected in the near future.
FPGAs are increasingly being used to accelerate AI and machine learning workloads. Their reconfigurable nature allows for optimized hardware implementations of AI models, resulting in faster inference and training times. FPGA-based solutions are being integrated into edge devices, data center servers, and cloud platforms to meet the growing demand for AI-powered applications.
Q7 – Ensuring the security and integrity of FPGA designs, especially in sensitive applications like finance and defense.
These days modern SRAM-based FPGAs have gained global prominence for their reprogrammability, allowing for flexible FPGA area configuration via bitstream files. However, this reprogrammability necessitates storing the bitstream file in non-volatile memory, raising security concerns. Unauthorized access to this file poses risks of hardware reverse engineering, particularly in network-based phishing attacks. To address this, contemporary FPGAs, including Xilinx 7 series devices, integrate robust bitstream encryption features. Managed via Vivado software, this encryption leverages dedicated on-chip AES decryption logic, enhancing design security. By making the FPGA’s proprietary logic inaccessible to unauthorized users, these encryption measures safeguard intellectual property, making modern FPGAs suitable for security-critical applications in defense and space industries.
Q8 – Advice for students and professionals interested in pursuing a career in FPGA development to stay updated with the latest trends and technologies.
Undergraduates aspiring to enter the FPGA industry should start with a strong foundation in digital logic design, learning how conditional logic circuits work at the gate level. Then, dive into a Hardware Description Language (HDL) like Verilog, known for its user-friendly syntax. To apply this knowledge, use the Xilinx Vivado EDA tool, which provides a free license and good documents for their tool.
Furthermore, you needed to learn the AXI4 Memory-Mapped (AXI4-MM) protocols that are used for IP core design within System-on-Chip (SoC) architecture. These protocols facilitate efficient communication between components. AMBA buses, such as AHB, APB, and AXI, are key in connecting various IP cores in an SoC. AXI4-MM is often used for high-performance memory-mapped communication. Simple peripherals, controlled by a CPU in an SoC, connect to these buses. Once you grasp this architecture, you can adapt your FPGA knowledge to work with ease across different vendors’ FPGAs, enabling versatile hardware development.
For research students looking to create FPGA-based hardware accelerators efficiently, Vitis HLS is a valuable and cost-effective tool. Ideal for AI and ML projects, it simplifies complex algorithm implementation without extensive low-level programming. This tool drastically cuts development time and expenses, empowering students to accelerate their research in FPGA-based hardware acceleration for AI and ML applications.
