BoolSi Raises $6M to Transform Software Into Custom Silicon Using AI

Hardware acceleration has long promised dramatic performance gains, but accessing those gains has traditionally required specialized expertise in chip design. Boston-based startup BoolSi is aiming to change that equation. The company has announced a $6 million seed round led by Fine Structure Ventures, with participation from Pillar VC, Fifth Quarter Ventures, and Coalition Ventures, as it works to make custom hardware acceleration accessible to software developers.

The funding will support the launch of BoolSi’s private beta later this year and accelerate development of a platform that automatically converts software into hardware accelerators running on Field Programmable Gate Arrays (FPGAs), without requiring engineers to learn hardware description languages or traditional chip design workflows.

Reimagining the Path from Code to Hardware

At the heart of BoolSi’s approach is a simple idea: software developers should be able to achieve hardware-level performance gains without becoming hardware engineers.

Rather than translating source code line by line into digital circuits, BoolSi trains machine learning models to learn the behavior of a program. Once the system understands the transformation between inputs and outputs, the neural network converges into a fully digital circuit that can be analyzed, verified, and optimized using existing chip design tools. According to the company, the resulting hardware is not an approximation of the original code but an exact implementation of its behavior.

The company describes this as a new compilation layer that sits between software and silicon. Developers identify a computational hotspot in C, C++, or other supported languages, and BoolSi generates a custom hardware accelerator alongside the required deployment artifacts. The result is a co-processor tailored specifically to that workload.

Why FPGAs Matter

BoolSi’s initial focus is on FPGAs, a category of reconfigurable chips that can be customized after manufacturing. FPGAs have become increasingly important in robotics, edge AI, and embedded systems because they can execute specialized workloads with far lower latency and power consumption than traditional CPUs.

Despite their advantages, FPGA development remains difficult for most software teams. Existing tools often require expertise in hardware description languages such as Verilog or VHDL, creating a steep learning curve that limits adoption. BoolSi is attempting to remove that barrier entirely by treating hardware generation as a compiler problem rather than a hardware design problem.

From Months of Engineering to an Afternoon

One of the company’s more striking examples involves a 10,000-line C regular expression library used to identify email addresses in a text stream. Running on an ARM Cortex-A9 processor, the workload completed in 2.66 milliseconds. Compiled into a BoolSi-generated hardware accelerator, execution time dropped to 0.325 milliseconds. Using eight parallel hardware agents reduced latency further to 0.042 milliseconds, representing a 63x improvement over the CPU baseline.

The broader vision extends beyond isolated acceleration tasks. BoolSi believes that many modern applications contain repetitive loops, protocol handlers, control systems, and processing kernels that could benefit from dedicated hardware. Rather than forcing developers to rewrite these components in specialized hardware languages, the company wants the original software itself to serve as the specification.

Robotics and Embedded Systems First

BoolSi is initially targeting embedded developers working in robotics, where latency, power efficiency, and real-time performance often determine whether a product succeeds. Applications such as sensor fusion, motor control, optical flow estimation, and model-predictive control are natural candidates for hardware acceleration.

The company’s long-term ambition is considerably larger. It envisions a future where every CPU is paired with reconfigurable logic that can be customized for the workloads running beside it. As AI-assisted software development continues to reduce the time required to create applications, BoolSi argues that the next major productivity leap may come from automatically converting those applications into specialized hardware.

If that vision proves achievable, the distinction between software engineering and hardware engineering could become far less rigid than it is today. For now, the company is focused on proving that custom silicon can become as accessible as writing code.