FPGA Insights has engaged in an exclusive interview with Ahmad Zaky Ramdani, FPGA Engineer at PT Elcarim Optronic Indonesia
1) Can you provide an overview of your organization and the services/products it offers?
Currently, I am working for a company that specializes in the development of devices, particularly for defense purposes. Our primary product lies within the realm of electro-optical systems. However, we’re currently expanding our product range to include integrated surveillance systems and weapon control systems.
My role involves designing and implementing various algorithms, particularly in signal processing and image processing. Additionally, I work on high-performance signal interfaces and dabble in some AI applications (Computer Vision).
2) Can you explain the benefits of using FPGAs over other types of processors?
Roughly, I enjoy using the analogy of FPGA implementation as building with clay compared to processor implementation as constructing with LEGO bricks.
Imagine building a model car, having wheel-shaped LEGOs readily available means one of your main challenges can be solved in a matter of seconds. However, if you don’t have them, you’re in a big problem. In contrast, with clay, if you don’t have the wheels, all you need to do is craft your own. In FPGA implementation, the limitation is only the quantity of your logic resources and your own ingenuity.
Additionally, unlike CPUs and GPUs, which execute software instructions sequentially, FPGAs perform parallel processing at the hardware level. This makes FPGAs highly efficient for applications where speed and parallelism are critical, such as image and signal processing, cryptography, and machine learning.
Nonetheless, it’s important to note that FPGA isn’t a one-size-fits-all solution. The decision on which solution to employ hinges entirely on the specific application at hand. In some cases, a rudimentary combination of capacitors and resistors might suffice, and FPGAs might not be necessary at all.
3) What are the most significant trends observed in the FPGA industry over the past year? How will these trends shape the industry’s future?
Over the past year, the FPGA industry has witnessed several significant trends. FPGAs have gained prominence in AI and machine learning acceleration, making them integral to AI infrastructure. They’ve found increased adoption in data centers for tasks like networking and security acceleration, thanks to their adaptability and energy efficiency. Customization capabilities have made FPGAs appealing for creating application-specific solutions, while high-level synthesis tools aim to simplify FPGA programming.
Security and reliability have driven FPGA use in hardware-based security solutions, and the integration of FPGAs with other processing units, known as heterogeneous computing, is becoming more prevalent. Ongoing advancements in FPGA architectures are yielding higher capacity, faster performance, and lower power consumption. These trends are expected to continue shaping the FPGA industry, expanding their applications and accessibility across various sectors, including emerging technologies like 5G, IoT, and edge computing.
4) How do you see FPGA development evolving to meet the demands of modern applications and complex workloads
In the past, FPGA development was complex and time-consuming, requiring expertise in hardware description languages like Verilog and VHDL. However, recent efforts have aimed to address this challenge by introducing alternative development options. Xilinx, for instance, has introduced HLS, which allows developers to translate common C-based programs into IP cores.
Additionally, tools like Simulink in MATLAB can generate RTL code, and there are Python-based frameworks such as MyHDL and Migen. FPGA vendors have also included system builders for specific applications, especially in AI, further simplifying and expediting the development process. The integration of hardened ARM cores has made development even more accessible by facilitating task distribution. This evolution has transformed FPGAs from simple devices into more complex and versatile platforms.
5) Key drivers behind the increasing adoption of FPGAs in various applications and industries?
My understanding revolves around the significant surge in the demand for substantial computing power and extensive data processing. This surge has forced the enhancement of capabilities within data centers, and this is precisely where FPGAs emerge as accelerators, offering the ability to handle complex tasks efficiently.
Moreover, plenty of applications are rapidly evolving, placing an emphasis on real-time data processing right at the edge of networks and devices. When traditional Microcontroller Units (MCUs) fall short in delivering the required throughput for these demanding tasks, FPGAs step in as a robust solution. This transformation is not limited to one specific sector; it is unfolding across various fields and industries, marking FPGAs as versatile and indispensable components in the modern computing landscape.
6) Sectors that stand to benefit the most from FPGA integration, and why?
Sectors like telecommunications, data centers, and AI will undoubtedly remain some of the primary sectors, but with massive digitization across various aspects of our lives, this is actually quite broad and becoming harder to categorize. The implementation of FPGA can be applied to various fields. Any sector that requires real-time processing, extensive customization, and low latency can benefit significantly from FPGA integration. Especially with the growing trend of SoC FPGA, which offers a broader range of implementations and faster time to market.
7) The role of FPGAs in accelerating AI applications and advancements expected in the near future.
FPGAs have emerged as pivotal tools in accelerating AI applications due to their inherent parallel processing capabilities. Their role in AI is set to grow even more significantly in the near future. FPGAs offer the flexibility to implement custom hardware architectures optimized for specific AI workloads, delivering substantial performance gains. As AI models become increasingly complex and demand higher computational power, FPGAs provide a scalable solution.
Moreover, the integration of FPGAs with high-level synthesis tools is simplifying FPGA programming, making it more accessible to software developers, thus widening their adoption in the AI community. Expectations for the future include continued advancements in FPGA architectures, resulting in even more powerful and energy-efficient solutions. Additionally, FPGAs are likely to play a crucial role in edge AI, enabling real-time processing and decision-making at the edge of the network, further expanding their influence in the AI landscape.
8) Ensuring the security and integrity of FPGA designs, especially in sensitive applications like finance and defense.
Certainly, safeguarding the security and integrity of FPGA (Field-Programmable Gate Array) designs is absolutely critical, to address this concern effectively, a comprehensive security strategy should be put in place. This entails secure development practices, stringent access controls, and a secure supply chain. Incorporating hardware security features, encryption, and robust key management helps fortify FPGA designs against potential threats like unauthorized access and tampering. Continuous monitoring, intrusion detection, and routine audits serve as proactive measures to spot vulnerabilities and respond promptly to any security risks.
Moreover, compliance with industry standards and compliance requirements, along with the use of secure communication protocols and physical security measures, adds an extra layer of protection. In these high-stakes sectors, it’s imperative to adopt a multi-faceted security strategy to safeguard FPGA designs and uphold data integrity, effectively mitigating the evolving landscape of security threats.
9) Advice for students and professionals interested in pursuing a career in FPGA development to stay updated with the latest trends and technologies.
FPGA technology evolves rapidly. Make a commitment to continuous learning, whether you are a student or a professional. Dedicate time regularly to explore new tools, technologies, and FPGA architectures. Apply your knowledge by working on hands-on FPGA projects. Personal or team projects allow you to gain practical experience and apply what you’ve learned.
One crucial aspect to always remember is that FPGA development is not akin to software programming; it’s more akin to hardware design. It’s essential to maintain this mindset. When writing source code, envision how your hardware will be structured. FPGA development has a steep learning curve, especially for those new to digital design. Understanding concepts like finite state machines, clock domains, and hardware pipelines can be challenging but it is fundamental. But don’t worry; just enjoy it, like you’re playing with clay. 😊