FPGA Innovations

FPGAs have long been essential for high-performance computing, AI acceleration, and signal processing—but scalability and efficiency have remained persistent challenges. Enter Monolithic 3D (M3D) FPGA architecture, a breakthrough leveraging stackable back-end-of-line (BEOL) transistors to redefine FPGA design. 🔍 What Makes M3D FPGAs Game-Changing? Traditional FPGAs rely on Si-based SRAM for configuration memory, but M3D architecture integrates: ✅ N-type (W-doped In₂O₃) and p-type (SnO) amorphous oxide semiconductor (AOS) transistors in the BEOL ✅ More compact and power-efficient pass gates for reconfigurable circuits ✅ FPGA switch and connection block matrices stacked above configurable logic blocks (CLBs) 💡 The Results? 📉 3.4x reduction in area-time squared product (AT²) ⚡ 27% lower critical path latency for faster execution 🔋 26% lower power consumption in reconfigurable routing blocks 🔬 Why This Matters for Future Applications With leading foundries investing in BEOL-compatible AOS transistors, M3D FPGAs are poised to: 🧠 Accelerate hyperdimensional computing and large language models (LLMs) 🌍 Enable ultra-efficient edge AI inference and real-time signal processing 📡 Revolutionize next-gen telecom, radar, and high-frequency trading systems 🔑 The Road Ahead By interfacing with Verilog-to-Routing (VTR) tools, M3D FPGA designs in 7 nm technology are already demonstrating next-level performance gains. As device research and circuit design converge, we’re looking at a new era of FPGA efficiency, scalability, and power optimization. ⚙️ How do you see M3D FPGAs shaping the future of reconfigurable computing?

Nine months ago, I published my paper on designing an AI hardware accelerator from scratch—a challenging yet rewarding journey. Unlike the common trends of binary computing, analog approaches, or neuromorphic designs, this project pushed the boundaries of digital logic by rethinking information theory. A deep integration of hardware-software co-optimization demanded versatility and proficiency in both high-level and low-level programming, and the creation of custom design automation to manage the vast complexity of CNN parameters. Recently, I came across one of my screenshots on MNIST CNN classification. It felt unreal at first but realizing that the results were on actual hardware turned that feeling into pride. It’s a glimpse into the potential of FPGA in future AI hardware by achieving CNN classification in nanoseconds. No AIE, no DSP, no BRAM, just pure LUT horsepower. https://lnkd.in/g8CrUJqX #AI #FPGA #Innovation

You can design a Linux capable SoC in an FPGA using mostly Python. LiteX is another bright example of the power of open-source. It is a Python based HDL framework created by Florent Kermarrec of Enjoy Digital, that simplifies the development of complex systems in FPGAs. It is designed to be portable and has an extensive list of supported boards from all the major FPGA vendors, and even some of the smaller ones. In fact, it is portable enough that it is also starting to be used with open-source ASIC flows. LiteX leverages Migen to describe digital logic with Python and has a growing library of portable IP that already includes all the pieces needed for a Linux capable SoC. The Linux on LiteX-VexRiscv project demonstrates this and already supports more than 40 different FPGA boards. This powerful tool makes FPGAs accessible for Python developers. #FPGA #opensource #python #LiteX #Linux