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Silicon CDMA Sensing architectures and IP

The Sensing Intelligence Platform

The Sensing Intelligence Platform

ADCs

CogniSense delivers Tensor‑ΣΔ ADCs that capture continuous analog signal information using Tensor-ΣΔ acquisition and CDMAX multi-channel encoding.

HMI Displays

CogniSense’s Tensor‑ΣΔ display assemblies integrate touch, force, and spatial sensing into a single sensing architecture tailored for next‑generation devices.

Auto & Grid

Tensor‑ΣΔ sensing solutions fuse multi‑modal inputs into high‑resolution, robust data streams that deliver distributed vehicle and grid sensing.

Robotics & Automation

Tensor‑ΣΔ sensing solutions integrate cross‑domain inputs into high‑resolution, robust data streams that enable precise, context‑rich analysis for workflows.

Tensor-ΣΔ ICs & IP developed analog front end platforms

Our general‑purpose Tensor‑ΣΔ AFEs are self‑configuring, adaptive, and designed to offload ML workloads to the host processor giving CogniSense Labs a clear sensing edge across consumer devices, wearables, and robotics. They deliver high‑fidelity, high‑dynamic‑range signals that remain stable through moisture, gloves, vibration, electrical noise, and other real‑world conditions.

Our patent pending integrated CDMAX ΣΔ (CDMA Extended) in the CogniSense architecture introduces simultaneous coded multi-channel acquisition, eliminating scan latency inherent in multiplexed sensing systems., enabling simultaneous multi-channel acquisition without the latency or bandwidth constraints of traditional polling methods.

From Digital Bottlenecks to Signal-preserving Sensing Architecture

  • Traditional consumer devices rely on on‑chip digital processing, digitizing signals after irreversible filtering removes information required for adaptive interpretation.
  • CogniSense Labs introduces a sigma‑delta analog front‑end (AFE) that captures high‑resolution, low‑noise raw signals directly at the sensor interface.

Digital Processing on the Host, Not AFE Silicon

  • Raw data streams on the host are processed by onboard AI models, enabling adaptive, context‑aware interpretation instead of fixed digital pipelines.
  • Reduces fixed digital preprocessing stages traditionally required before inference.

Signal Intelligence Starts at the Edge

The shift from low‑resolution, fixed‑firmware touch ICs to CogniSense’s Tensor-ΣΔ AFEs capture continuous touch physics, enabling software-defined interpretation beyond fixed firmware controllers. Resolution, noise immunity, scalability, and power efficiency enabling software‑defined touch go far beyond X,Y coordinates to interpret identity, intent, pressure, and biometrics.

The Problem: Sensors Lose Data Before Intelligence Begins

Most sensing systems digitize signals only after aggressive filtering, multiplexing, and compression. While necessary for legacy electronics, these steps permanently discard signal information required for accurate interpretation in noisy, dynamic environments.

As a result, modern systems rely on increasingly complex digital processing and AI models to compensate for information that has already been lost. The limitation is not software but it is acquisition architecture.

Our Approach: Signal Intelligence Starts at the Edge

CogniSense Labs introduces Tensor-ΣΔ, a continuous-time analog front-end architecture designed to preserve signal fidelity at the moment of measurement.

Instead of scanning sensors sequentially or reducing signals prematurely, Tensor-ΣΔ captures structured multi-channel signal data that retains temporal and spatial relationships across sensing domains. This enables software and AI systems to interpret richer physical information using less computational effort.

Why Conventional Architectures Fail: Digital Processing Begins Too Late

Conventional Sensor Interfaces Tensor-ΣΔ Architecture
Sequential scanning Simultaneous Coded Acquisition
Early Filtering Signal Preservation
Fixed Pipelines Adaptive Interpretation
High Digital Workload Reduced Compute Overhead
Noise Mitigation After Digitizing Statistical Siparation At Aquisition

When information is preserved early, intelligence becomes simpler later.

The Problem: Sensors Lose Data Before Intelligence Begins

Most sensing systems digitize signals only after aggressive filtering, multiplexing, and compression. While necessary for legacy electronics, these steps permanently discard signal information required for accurate interpretation in noisy, dynamic environments.

As a result, modern systems rely on increasingly complex digital processing and AI models to compensate for information that has already been lost. The limitation is not software but it is acquisition architecture.

Our Approach: Signal Intelligence Starts at Acquisition & Edge

CogniSense Labs introduces Tensor-ΣΔ, a continuous-time analog front-end architecture designed to preserve signal fidelity at the moment of measurement.

Instead of scanning sensors sequentially or reducing signals prematurely, Tensor-ΣΔ captures structured multi-channel signal data that retains temporal and spatial relationships across sensing domains. This enables software and AI systems to interpret richer physical information using less computational effort.

Why Conventional Architectures Fail: Digital Processing Begins Too Late

Conventional Sensor Interfaces Tensor-ΣΔ Architecture
Sequential scanning Simultaneous Coded Acquisition
Early Filtering Signal Preservation
Fixed Pipelines Adaptive Interpretation
High Digital Workload Reduced Compute Overhead
Noise Mitigation After Digitizing Statistical Siparation At Aquisition

When information is preserved early, intelligence becomes simpler later.

Why choose us

We wrote the book on intelligent surfaces using sigma-delta technologies.

We Design Signal Acquisition Architectures That Enable Intelligent Interfaces

CogniSense Labs was founded on the belief that touch should be understood, not just detected. Our signal-preserving sensing architecture captures pressure, force, motion, and intent with extraordinary fidelity—giving our partners capabilities that redefine what digital surfaces can do

Architectures Designed for Automotive-Grade Noise, Variability, and Manufacturing Scale

Our team blends deep R&D expertise with decades of experience in manufacturing, compliance, and productization. Every solution we deliver is designed to scale, survive harsh environments, and meet the rigorous standards of automotive, medical, industrial, and consumer markets.

We License Architecture Platforms and Support OEM Integration

We don’t hand over a generic sensor and walk away. We collaborate closely with OEMs and product teams to tailor sensing architectures, mechanical integration, firmware, and AI models to their exact requirements. Your product vision becomes the blueprint for our engineering.

Enable New Interaction Modalities Through High-Resolution Signal Access

Our technology transforms passive surfaces into intelligent, expressive interfaces, enabling pressure‑aware displays, adaptive controls, safety‑critical systems, and entirely new HMI experiences. We help our partners differentiate through features and better interaction design.

Revolutionizing Sensing Through Signal Intelligence & AI

“We don’t fight noise – we make it irrelevant“

CogniSense Labs brings deep expertise in sensing and high‑resolution signal acquisition. By working directly with raw sensor data and applying signal inference analytics, the platform unifies multiple sensing modalities into a single intelligent signal‑processing layer delivering precision and insight conventional architectures can’t match.

Tensor‑ΣΔ & CDMAX Key Capabilities

  • Continuous Signal Capture

    Eliminates scan latency and preserves temporal behavior across channels.

  • Multi-Modal Acquisition

    Supports touch, force, motion, and environmental sensing within a unified architecture.

  • Adaptive Calibration

    Continuous channel characterization enables self-calibrating systems in changing environments.

  • Efficient Inference

    Higher signal fidelity reduces preprocessing and improves downstream model efficiency.

Elevating the Cockpit to a Smart HMI Network

No need to design different MCUs for different devices → the architecture scales inherently

Tensor‑ΣΔ AFEs efficiency turn the automotive cockpit into a real‑time intelligence environment. By keeping all sensor, touch, gesture, and environmental signals in a unified multi-modal signal representation derived from shared Tensor-ΣΔ acquisition., the system executes faster inference, lower‑latency control, and cleaner signal fusion directly at the edge. This reduces SoC load, cuts integration complexity, and delivers a cockpit that responds instantly and consistently to driver intent & more precise, more adaptive, and fundamentally more intuitive.

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We’re at the start of making sensing simpler, smarter, and ready for building next‑gen products with Tensor‑ΣΔ.