The Race To Ultra Low Power AI Begins With POLYN Technology’s NASP Breakthrough

Q: What does the successful silicon validation of NASP technology represent for POLYN Technology’s vision in advancing ultra low power AI solutions?

A:
Successful validation in silicon is one of the most significant milestones in any semiconductor company's journey. For POLYN Technology, it represents the transition from breakthrough research to proven commercial reality. NASP is no longer simply an innovative concept or simulation; it is a fully functional chip delivering measurable performance under real world operating conditions.

The validation demonstrated that neural networks can be implemented directly in analog hardware while maintaining accuracy and stability. This achievement confirms the robustness of POLYN's design methodology, automated synthesis flow, and analog calibration techniques. It also validates the company's fundamental thesis: that analog neuromorphic computing can outperform conventional digital architectures for always on sensing applications.

Measured silicon results showed ultra low power consumption of 35 microwatts, exceptionally low neural network inference latency of 20 microseconds, and highly deterministic behavior of more than 95% accuracy. These characteristics are precisely what OEMs require for next generation wearable, industrial, and automotive products.

Equally important, validation substantially lowers adoption risk for prospective customers. Customers demand proven hardware, repeatable manufacturing, and predictable system integration. Successful tapeout and validation provide this confidence.

For investors, partners, and ecosystem participants, this milestone confirms that POLYN is executing against its roadmap. It transforms the conversation from “Can this work?” to “How quickly can this scale?”

Getting the silicon chips to perform exactly as modeled is a huge achievement. There were several challenges to solve in all aspects of NASP and its application.

First, it was critical to master the convergence of digital neural architectures into physical analog circuits while preserving accuracy and amplifying performance. There was a need to handle non linearity and process variations that naturally occur in analog architectures. The engineers working on this developed proprietary circuit design techniques that make NASP robust.

As the chips are always application specific, it was necessary to learn in depth the requirements of each application and the corresponding data science: collect relevant labeled and unlabeled data and master math to extract only useful information from raw datasets. To do this, it was required to become part of the application ecosystem. It’s always an exciting yet time consuming journey.

As a result, a proprietary tool converts a trained digital neural network into an accurate mathematical model of the chip. This way, there is a digital twin for every new chip product. Customers use digital twins for testing and model validation.

Then, in close cooperation with EDA vendor Cadence, POLYN developed new approaches to using standard tools, resulting in a chip design flow tailored to analog processing tasks. This specially designed EDA flow is crucial for delivering the first silicon for each application and ensuring a quick time to market for the chip.

Ultimately, successful validation establishes NASP as a credible new computing platform capable of reshaping the economics of edge AI across multiple industries.

Q: How does NASP technology help enable new possibilities for always on edge devices across applications such as wearables, industrial sensing, and automotive systems?

A:
Always on devices face a relentless engineering challenge: they must continuously sense, analyze, and respond while consuming virtually no power. Traditional digital AI solutions often force unacceptable tradeoffs between intelligence and battery life. NASP removes this limitation.

In wearable devices, continuous monitoring becomes practical without sacrificing user experience. Smart earbuds can continuously detect voice activity. Fitness trackers can analyze biosignals around the clock. Medical wearables can detect anomalies in ECG, EMG, or motion patterns while operating for days or weeks between charges.

Industrial IoT systems benefit even more dramatically. Vibration sensors can continuously monitor rotating equipment, detecting early signs of bearing wear, imbalance, cavitation, or misalignment. Since inference occurs locally, there is no need to stream raw data to the cloud, dramatically reducing bandwidth requirements and improving reliability in remote installations.

Automotive systems represent another compelling domain. Tire road friction estimation requires real time processing under strict power and latency constraints. NASP, which leverages neural networks to process high frequency tire vibration data and extract only meaningful embeddings, accelerates the adoption of intelligent, physics aware sensing for next generation ADAS and autonomous driving platforms. As an example, the POLYN VibroSense tire monitoring solution operates in conjunction with a TPMS SoC, using accelerometer signals to monitor tire road grip.

This solution is developed with extensive real world validation. The system is trained on diverse driving conditions to estimate the peak friction coefficient in real time, enabling predictive insight into road grip before critical events such as braking occur. Validation through controlled braking scenarios, along with correlation with industry benchmarks, demonstrates strong alignment with real world friction behavior across standard surfaces, including asphalt, concrete, ice, and aquaplaning conditions. The solution is engineered to maintain reliable performance under complex and non ideal driving conditions, such as wet roads or uneven terrain, providing stable estimates with confidence aware outputs.

In robotics, modeling a reflex arc with NASP chips brings an always on layer of intelligence directly to the boundary between sensors and actuators. Rather than transmitting raw streams of tactile, torque, or vibration data, this layer continuously distills the signal into meaningful features, such as slip onset, abrupt force anomalies, resonance patterns, or sudden changes in contact.

In this role, NASP functions as the high speed sensory limb of a reflex system. Its strength lies in deterministic, ultra low latency processing that isolates critical information early enough for the system to respond before conditions worsen. The extracted features are passed to a local control layer that determines corrective actions adjusting grip, modulating impedance, retracting along a safe path, freezing motion, or limiting torque.

This approach effectively creates a two tier robotic architecture. A higher level AI handles reasoning, planning, and adaptation, while the NASP based reflex layer manages fast physical interactions locally, operating at microsecond latency and microwatt power with consistent timing.

The overall impact is a reduction in data flow, lower computational burden, and improved responsiveness and safety. It introduces a functional equivalent of a spinal cord into robotic systems enabling rapid, dependable physical reactions and moving robotics closer to scalable, real world deployment.

Beyond these examples, NASP unlocks entirely new product categories. Battery less sensors powered by energy harvesting become feasible. Disposable medical diagnostics gain embedded intelligence. Autonomous robots can distribute sensing intelligence across hundreds of local nodes.

The result is a profound shift: intelligence is no longer centralized, it becomes intrinsic to every sensor.

Q: Could you share how your approach to analog neuromorphic computing differentiates POLYN from conventional digital AI chip solutions?

A:
Conventional AI accelerators rely on digital abstraction. They simulate neural networks using binary arithmetic, memory arrays, and clocked logic. This works well at high power levels but becomes increasingly inefficient at the edge.

Every inference involves repeated data movement between memory and compute units. Each multiply accumulate operation consumes switching energy. ADCs, SRAM, clock trees, and control logic further increase system overhead.

NASP takes a fundamentally different approach.

Instead of emulating neural networks digitally, NASP physically embodies them in analog circuitry. Computation occurs through current flow, where the laws of physics perform the neural math naturally and simultaneously.

This provides several decisive advantages. Power consumption drops by orders of magnitude, latency shrinks dramatically, and system complexity decreases because large memory arrays and high speed digital data paths are unnecessary.

NASP operates asynchronously, eliminating the need for a global clock. Each neuron responds only when relevant signals are present, much like biological neural systems.

Importantly, neural networks are inherently tolerant to analog elements variation. This resilience can be leveraged through calibration aware training and proprietary mapping algorithms.

The result is a computing architecture purpose built for the physical world, rather than adapted from digital computing paradigms developed decades ago.

Q: How are you working with customers and partners to accelerate adoption of NASP based chips and evaluation kits?

A:
Commercial success depends not only on breakthrough hardware but also on ecosystem readiness. POLYN has built a comprehensive go to market strategy centered on customer enablement.

Evaluation kits provide immediate hands on access to NASP technology. Customers can rapidly benchmark power consumption, latency, and inference accuracy against incumbent digital solutions. This shortens qualification cycles and accelerates proof of concept development.

The software tool chain integrates with familiar machine learning environments, allowing developers to train models using standard frameworks before compiling them into NASP implementations. It is tremendously important to collaborate closely with customers throughout the design cycle from application feasibility studies and neural architecture optimization to board level integration and production ramp.

Strategic partnerships with sensor manufacturers, semiconductor vendors, foundries, packaging houses, and module suppliers ensure robust supply chain support.

Reference designs tailored for specific applications further simplify deployment.

In addition, the active engagement of system integrators and design service partners extends market reach and support for regional customers.

This collaborative model enables customers to move from concept to production rapidly while minimizing technical and commercial risk.

Q: What are your expectations for industry engagement and collaboration opportunities as you showcase this technology at CES 2026?

A:
CES 2026 provided a global platform to demonstrate that analog AI has moved beyond theory into commercial reality. Strong engagement was expected across multiple stakeholder groups, including OEMs, semiconductor companies, sensor suppliers, investors, and software developers.

CES made it clear that the market is actively seeking solutions that can deliver meaningful AI performance under stringent power constraints. NASP directly addresses this demand.

Live demonstrations showcased compelling real world applications, including voice detection, industrial condition monitoring, and sensor intelligence operating at unprecedented power levels.

As an exhibitor, we anticipated particularly strong interest from companies developing next generation wearables, smart appliances, automotive electronics, and industrial sensing platforms.

CES also serves as an important venue for ecosystem building. Discussions with foundry partners, packaging providers, design houses, and channel partners help accelerate commercialization.

Beyond immediate commercial opportunities, CES offers strategic visibility. It allows companies such as ours to shape industry perception around analog AI and establish thought leadership in a rapidly emerging category.

We expect that in the aftermath of CES 2026, we will see the generation of new pilot programs, design wins, strategic alliances, and long term collaboration opportunities that significantly expand NASP adoption worldwide.

The most recent news is the automotive NASP chip tapeout with GlobalFoundries, using more advanced CMOS nodes for this and the next generation of products. This is another milestone, bringing VibroSense one step closer to customer adoption. This answers the expectations of Tier 1 suppliers and automotive OEMs that believe that indirect calculations and virtual sensors cannot provide the required accuracy under rapidly changing road conditions, particularly in autonomous driving scenarios, and seek to obtain real time tire road friction estimates directly from the tire.

Q: What message would you like to convey to developers and OEMs about the future potential of analog AI and POLYN’s role in shaping it?

A:
The future of artificial intelligence will be distributed, autonomous, and deeply integrated into the physical world. This transition requires new hardware architectures designed specifically for sensor centric computing.

As the number of intelligent sensors grows into the tens of billions, conventional digital approaches will face insurmountable limitations in energy, latency, and cost. Analog neuromorphic computing solves these challenges at their source.

POLYN is pioneering this transformation by allowing developers to deploy sophisticated intelligence where it matters most directly at the sensor interface.

For OEMs, this creates enormous opportunities: longer battery life, lower BOM, faster response times, improved privacy, and entirely new product categories.

For developers, NASP opens the door to innovative applications that were previously impractical or impossible.

But commitment must extend beyond technology. That encompasses building the tools, ecosystem, and partnerships necessary to make analog AI accessible, scalable, and commercially successful.

The next decade of edge intelligence will belong to architectures that seamlessly unite sensing and computation. POLYN’s objective is to lead that future.