Multiplication is the cornerstone of AI math, but it’s also a major energy hog. For cutting-edge models like large language models (LLMs), the sheer volume of multiplications required can become a bottleneck. Now, researchers are exploring clever ways to approximate multiplication, trading a bit of precision for significant gains in speed and energy efficiency. One promising technique called L-Mul (short for linear-complexity multiplication) replaces complex multiplications with simpler additions and shifts. Think of it like rounding off numbers before calculating – you lose a tiny bit of accuracy, but the calculations become much faster. This research explores implementing L-Mul in hardware on Field-Programmable Gate Arrays (FPGAs). FPGAs are like blank canvases for hardware design, allowing for highly customized circuits. Researchers crafted an L-Mul implementation specifically optimized for the FP8 number format. FP8 (8-bit floating point) is a rising star in AI because it offers a good balance between precision and efficiency. This new hardware implementation for L-Mul shrinks the resource usage on the FPGA, essentially making the calculations take up less space and use less power. The results show a significant reduction in power consumption, sometimes as much as 15%, compared to traditional methods, without a drastic drop in accuracy. This is a win-win for AI acceleration! This clever approximation technique can free up resources and power, paving the way for even more powerful and efficient AI models. The ability to run larger, more complex models with less energy opens doors for exciting applications in various fields, especially in resource-constrained environments like mobile devices. This research focused on CNN and GCN accelerators, with future plans to explore even more demanding applications like LLMs and diffusion models. As the demand for AI processing grows, innovations like L-Mul will be essential for a sustainable AI future.