CardioNeural-SoC: An Indigenous RISC-V-Based Mixed-Signal System- on-Chip for Multi-Modal Cardiac and Neurological Monitoring — Architecture, RTL Verification, and Pre-Silicon Synthesis Results

In the vast landscape of modern medicine, the ability to listen to the body's electrical signals is a cornerstone of diagnosis. The heart beats with a rhythmic electrical pulse that doctors can trace to spot dangerous irregularities, while the brain fires with complex patterns that reveal the onset of seizures. For decades, capturing these signals has required bulky equipment or a collection of separate, specialized chips, each designed to listen to only one part of the body. In India, where millions suffer from undiagnosed heart conditions and epilepsy, this reliance on imported technology has kept advanced monitoring out of reach for many rural clinics and smaller hospitals. The cost of importing these specialized electronic components is high, and the supply chain is fragile, leaving a critical gap in the nation's healthcare infrastructure. The solution to this problem lies not in building better individual sensors, but in combining them into a single, unified device that can listen to the heart, the brain, and the blood flow simultaneously, all while being affordable enough to be manufactured locally.

A team of researchers at Hindusthan College of Engineering and Technology and Karpagam Faculty of Medical Sciences has taken a significant step toward this goal by designing a new type of computer chip called CardioNeural-SoC. This is not a finished product that has been mass-produced or tested on patients yet; rather, it is a complete digital blueprint and a rigorous simulation of a device that could change how medical monitoring works in India. The researchers have created a design for a single, tiny piece of silicon that integrates six different systems into one. This chip is intended to replace the six to eight separate components currently needed to build a multi-parameter patient monitor. By combining the ability to read heart signals, brain waves, and blood oxygen levels onto one die, the design aims to slash the cost of these devices, making them accessible to the thousands of small health centers that currently lack the budget for imported technology.

The core of this innovation is a mixed-signal system built on a 28-nanometer manufacturing process, a standard size for modern, efficient electronics. The design incorporates four channels for reading the heart's electrical activity, two channels for the brain, and a sensor interface for measuring blood flow and oxygen. These analog inputs are converted into digital data by a high-precision converter array and then processed by a custom digital brain based on the RISC-V architecture, an open-source instruction set that allows for flexible and cost-effective design. This digital core does more than just store data; it includes specialized hardware accelerators that can instantly analyze the signals to detect heart rhythm problems and the early signs of a seizure. To ensure the data remains secure as it is sent wirelessly to a doctor's device, the chip also includes a built-in encryption system and a low-power Bluetooth connection.

The researchers did not stop at drawing the circuit; they built a complete virtual model of the entire system and ran it through a series of nine different tests to ensure every part worked together correctly. In these simulations, the chip successfully managed the complex task of sampling heart and brain signals at different speeds, converting them to digital numbers, and then encrypting the results for transmission. The tests confirmed that the chip could correctly identify simulated heart arrhythmias and classify seizure onsets, all while managing its own power consumption. The digital logic was so thoroughly checked that every single test case passed, proving that the design functions as intended in a virtual environment before any physical chip is ever made.

To understand the physical scale of this achievement, the team synthesized the design for a specific part of the chip responsible for analyzing the energy of the signals. This section, which acts as a feature extractor for the classification algorithms, was found to contain over 21,000 individual logic gates and memory elements. When laid out, this specific block would occupy a tiny area of less than 300,000 square micrometers. In terms of power, this block was estimated to consume about 46 milliwatts when running at full speed, a figure dominated by the switching activity of its internal registers. The timing analysis showed that the data could flow through the most critical path of this circuit exactly on time, with zero margin for error at the target speed of 200 million cycles per second. This zero-margin result is a known challenge that the team acknowledges will need to be addressed when the physical layout is finalized, but it confirms that the design is on the edge of feasibility.

The motivation behind this work extends beyond technical specifications to a clear economic and social reality. The researchers estimated that India currently imports medical monitoring chips worth approximately 427 crore rupees annually across five major device categories, including intensive care monitors and wearable patches. By replacing these imported components with a domestically designed chip, the project aims to capture a significant portion of this market, potentially saving the healthcare system hundreds of crores each year. This cost reduction is not just about profit; it is about accessibility. The design targets a price point that would allow rural health centers to afford the same level of monitoring currently reserved for elite hospitals, potentially bringing diagnosis to the millions of people with epilepsy and heart disease who currently go untreated.

Despite the successful simulations and the promising market analysis, the paper is careful to state what has not yet been achieved. No physical chip has been manufactured, and no measurements have been taken from a real silicon device. The analog components, such as the amplifiers that boost the faint electrical signals from the skin, remain at the architectural specification stage and have not yet been built or tested. The researchers explicitly note that the power figures and timing results are based on computer models and that the final performance will only be known after the chip is fabricated and tested in a laboratory. The design has not yet been validated on human patients, and the clinical claims regarding its ability to diagnose disease are future goals rather than current facts.

The path forward for the CardioNeural-SoC project involves moving from these digital blueprints to the physical world. The next steps include designing the custom analog circuits, arranging the physical layout of the chip, and sending the final design to a fabrication facility in Chandigarh for a trial run. Once a physical chip is produced, it will undergo rigorous testing to verify that it meets the performance targets for noise, signal clarity, and power efficiency. Only after these physical validations and a pilot clinical study involving patients will the device be ready for the market. Until then, the work stands as a comprehensive proof of concept, demonstrating that a single, indigenous chip capable of monitoring both the heart and the brain is technically possible and ready for the next phase of development.