Hussein Osman is a semiconductor industry veteran with over 20 years of experience bringing to market silicon and software products that integrate sensing, processing and connectivity solutions, specializing in modern experiences that deliver value to the tip user. Over the past five years he has led the sensAI solution strategy and go-to-market efforts at Lattice Semiconductor, creating high-performance AI/ML applications. Mr. Osman received his bachelor’s degree in Electrical Engineering from California Polytechnic State University in San Luis Obispo.
Lattice Semiconductor (LSCC -12.36%) is a provider of low-power programmable solutions used across communications, computing, industrial, automotive, and consumer markets. The corporate’s low-power FPGAs and software tools are designed to assist speed up development and support innovation across applications from the Edge to the Cloud.
Edge AI is gaining traction as firms seek alternatives to cloud-based AI processing. How do you see this shift impacting the semiconductor industry, and what role does Lattice Semiconductor play on this transformation?
Edge AI is completely gaining traction, and it’s due to its potential to really revolutionize entire markets. Organizations across a big selection of sectors are leaning into Edge AI since it’s helping them achieve faster, more efficient, and safer operations — especially in real-time applications — than are possible with cloud computing alone. That’s the piece most individuals are inclined to concentrate on: how Edge AI is changing business operations when implemented. But there’s this other journey that’s happening in tandem, and it starts far before implementation.
Innovation in Edge AI is pushing original equipment manufacturers to design system components that may run AI models despite footprint constraints. Meaning lightweight, optimized algorithms, specialized hardware, and other advancements that complement and/or amplify performance. That is where Lattice Semiconductor comes into play.
Our Field Programmable Gate Arrays (FPGAs) provide the highly adaptable hardware crucial for designers to fulfill strict system requirements related to latency, power, security, connectivity, size, and more. They supply a foundation on which engineers can construct devices able to keeping mission-critical Automotive, Industrial, and Medical applications functional. This can be a big focus area for our current innovation, and we’re excited to assist customers overcome challenges and greet the era of Edge AI with confidence.
What are the important thing challenges that companies face when implementing Edge AI, and the way do you see FPGAs addressing these issues more effectively than traditional processors or GPUs?
You realize, some challenges appear to be truly universal as technology advances. For instance, developers and businesses hoping to harness the facility of Edge AI will likely grapple with common challenges, resembling:
- Resource management. Edge AI devices must perform complex processes reliably while working inside increasingly limited computational and battery capacities.
- Although Edge AI offers the privacy advantages of local data processing, it raises other security concerns, resembling the potential for physical tampering or the vulnerabilities that include smaller-scale models.
- Edge AI ecosystems might be extremely diverse in hardware architectures and computing requirements, making it difficult to streamline points like data management and model updates at scale.
FPGAs offer businesses a leg up in addressing these key issues through their combination of efficient parallel processing, low power consumption, hardware-level security capabilities, and reconfigurability. While these may sound like marketing buzzwords, they’re essential features for solving top Edge AI pain points.
FPGAs have traditionally been used for functions like bridging and I/O expansion. What makes them particularly well-suited for Edge AI applications?
Yes, you’re exactly right that FPGAs excel within the realm of connectivity — and that’s a part of what makes them so powerful in Edge AI applications. As you mentioned, they’ve customizable I/O ports that allow them to interface with a big selection of devices and communication protocols. On top of this, they’ll perform functions like bridging and sensor fusion to make sure seamless data exchange, aggregation, and synchronization between different system components, including legacy and emerging standards. These functions are particularly vital as today’s Edge AI ecosystems grow more complex and the necessity for interoperability and scalability increases.
Nevertheless, as we’ve been discussing, FPGAs’ connectivity advantages are only the tip of the iceberg; it’s also about how their adaptability, processing power, energy efficiency, and security measures are driving outcomes. For instance, FPGAs might be configured and reconfigured to perform specific AI tasks, enabling developers to tailor applications to their unique needs and meet evolving requirements.
Are you able to explain how low-power FPGAs compare to GPUs and ASICs when it comes to efficiency, scalability, and real-time processing capabilities for Edge AI?
I won’t pretend that hardware like GPUs and ASICs don’t have the compute power to support Edge AI applications. They do. But FPGAs truly have an “edge” on these other components in other areas like latency and adaptability. For instance, each GPUs and FPGAs can perform parallel processing, but GPU hardware is designed for broad appeal and isn’t as well suited to supporting specific Edge applications as that of FPGAs. Then again, ASICs targeted for specific applications, but their fixed functionality means they require full redesigns to accommodate any significant change in use. FPGAs are purpose-built to offer the perfect of each worlds; they provide the low latency that comes with custom hardware pipelines and room for post-deployment modifications every time Edge models need updating.
In fact, no single option is the right one. It’s as much as each developer to determine what is smart for his or her system. They need to fastidiously consider the first functions of the appliance, the precise outcomes they try to fulfill, and the way agile the design must be from a future-proofing perspective. This may allow them to decide on the appropriate set of hardware and software components to fulfill their requirements — we just occur to think that FPGAs are often the appropriate alternative.
How do Lattice’s FPGAs enhance AI-driven decision-making at the sting, particularly in industries like automotive, industrial automation, and IoT?
FPGAs’ parallel processing capabilities are a superb place to start. Unlike sequential processors, the architecture of FPGAs allows them to perform many tasks in parallel, including AI computations, with all of the configurable logic blocks executing different operations concurrently. This permits for the high throughput, low latency processing needed to support real-time applications in the important thing verticals you named — whether we’re talking about autonomous vehicles, smart industrial robots, and even smart home devices or healthcare wearables. Furthermore, they might be customized for specific AI workloads and simply reprogrammed in the sphere as models and requirements evolve over time. Last, but not least, they provide hardware-level security measures to make sure AI-powered systems remain secure, from boot-up to data processing and beyond.
What are some real-world use cases where Lattice’s FPGAs have significantly improved Edge AI performance, security, or efficiency?
Great query! One application that I find really intriguing is the ways engineers are using Lattice FPGAs to power the following generation of smart, AI-powered robots. Intelligent robots require real-time, on-device processing capabilities to make sure secure automation, and that’s something Edge AI is designed to deliver. Not only is the demand for these assistants rising, but so is the complexity and class of their functions. At a recent conference, the Lattice team demonstrated how the usage of FPGAs allowed a sensible robot to trace the trajectory of a ball and catch it in midair, showing just how briskly and precise these machines might be when built with the appropriate technologies.
What makes this so interesting to me, from a hardware perspective, is how design tactics are changing to accommodate these applications. For instance, as a substitute of relying solely on CPUs or other traditional processors, developers are starting to integrate FPGAs into the combo. The predominant profit is that FPGAs can interface with more sensors and actuators (and a more diverse range of those components), while also performing low-level processing tasks near these sensors to release the predominant compute engine for more advanced computations.
With the growing demand for AI inference at the sting, how does Lattice ensure its FPGAs remain competitive against specialized AI chips developed by larger semiconductor firms?
There’s little doubt that the pursuit of AI chips is driving much of the semiconductor industry — just have a look at how firms like Nvidia pivoted from creating video game graphics cards to becoming AI industry giants. Still, Lattice brings unique strengths to the table that make us stand out at the same time as the market becomes more saturated.
FPGAs usually are not only a component we’re selecting to speculate in because demand is rising; they’re a critical piece of our core product line. The strengths of our FPGA offerings — from latency and programmability to power consumption and scalability — are the results of years of technical development and refinement. We also provide a full range of industry-leading software and solution stacks, built to optimize the usage of FPGAs in AI designs and beyond.
We’ve refined our FPGAs through years of continuous improvement driven by iteration on our hardware and software solutions and relationships with partners across the semiconductor industry. We’ll proceed to be competitive because we’ll keep true to that path, working with design, development, and implementation partners to make sure that we’re providing our customers with essentially the most relevant and reliable technical capabilities.
What role does programmability play in FPGAs’ ability to adapt to evolving AI models and workloads?
Unlike fixed-function hardware, FPGAs might be retooled and reprogrammed post-deployment. This inherent adaptability is arguably their biggest differentiator, especially in supporting evolving AI models and workloads. Considering how dynamic the AI landscape is, developers have to have the opportunity to support algorithm updates, growing datasets, and other significant changes as they occur without worrying about constant hardware upgrades.
For instance, FPGAs are already playing a pivotal role in the continuing shift to post-quantum cryptography (PQC). As businesses brace against looming quantum threats and work to interchange vulnerable encryption schemes with next-generation algorithms, they’re using FPGAs to facilitate a seamless transition and ensure compliance with recent PQC standards.
How do Lattice’s FPGAs help businesses balance the trade-off between performance, power consumption, and price in Edge AI deployments?
Ultimately, developers shouldn’t have to choose from performance and possibility. Yes, Edge applications are sometimes hindered by computational limitations, power constraints, and increased latency. But with Lattice FPGAs, developers are empowered with flexible, energy efficient, and scalable hardware that’s greater than able to mitigating these challenges. Customizable I/O interfaces, for instance, enable connectivity to varied Edge applications while reducing complexity.
Post-deployment modification also makes it easier to regulate to support the needs of evolving models. Beyond this, preprocessing and data aggregation can occur on FPGAs, lowering the facility and computational strain on Edge processors, reducing latency, and in turn lowering costs and increasing system efficiency.
How do you envision the long run of AI hardware evolving in the following 5-10 years, particularly in relation to Edge AI and power-efficient processing?
Edge devices will have to be faster and more powerful to handle the computing and energy demands of the ever-more-complex AI and ML algorithms businesses have to thrive — especially as these applications change into more commonplace. The capabilities of the dynamic hardware components that support Edge applications might want to adapt in tandem, becoming smaller, smarter and more integrated. FPGAs might want to expand on their existing flexibility, offering low latency and low power capabilities for higher levels of demand. With these capabilities, FPGAs will proceed to assist developers reprogram and reconfigure with ease to fulfill the needs of evolving models — be they for more sophisticated autonomous vehicles, industrial automation, smart cities, or beyond.
