Ternara Semiconductor

Intelligence inside everyday things.

Ternara develops compute cores for home devices that can process data and commands locally, without a constant connection to the cloud.

We start with a smart air conditioner: clear commands, a comfortable temperature and data processed inside the device.

Pre-order chips See the scenario Discuss a pilot →

Application concept · Concept visualization

Scenario · air conditioner

Say it. The room follows.

“Set 22 degrees and switch on quiet mode.”

The command stays at home. In the target architecture the device works out what you want on its own and hands a permitted command to the air-conditioner controller.

The path of one command

You A short command in your own words.
Speech to text A speech recognizer in the device. A separate component.
Ternara core Chooses one of the permitted commands: temperature, mode, fan. This is the part we are developing.
Device controller Checks the command against its own limits and protections, then applies it.
Result The room cools to 22 degrees in quiet mode.

Interface demo

Try the interface

Choose a command:

Target24°

ModeCool

Fan—

Timer—

Permitted command —

Interface demo: a software model (JavaScript), not a chip output. It does not control a real air conditioner.

Works without internet Control stays available when the connection drops.
Data stays home Commands are processed inside the device, not on a remote server.
Simple to use Plain words instead of menus and remote-control codes.

Energy savings, response time and free-form speech are development goals. We will state them as results only after measurement.

Beyond air conditioning

Climate Heaters, fans and ventilation: “Warmer in the bedroom, quieter at night.”

Lighting “Dim the living room for the evening.”

Home sensors “Tell me if the humidity in the bathroom stays high.”

Technology

Minus one. Zero. Plus one.

−1 subtract

0 skip

+1 add

Neural networks multiply a great many numbers. Ternara cores use ternary weights: every weight is only −1, 0 or +1. Multiplying by such a weight means subtract, skip or add, so the arithmetic needs no large multipliers and the weights take much less memory.

Less memory A ternary weight is stored in 2 bits instead of the usual 16, so the same memory holds 8 times more weights.
Simpler arithmetic Add, subtract or skip instead of a full multiplication.
From a written specification Each block starts as a specification. The hardware description is generated from it and checked against reference models.

Future direction

Ternara τ: AI compute for computers, games and servers.

We plan to develop the Ternara τ compute core into a specialised accelerator for compatible neural network models. The prospect is local AI features on computers and game systems, and neural network inference on servers.

The goal is efficient execution of selected models with ternary weights. The architecture, software support and benefits are to be confirmed on prototypes.

Computers Possible acceleration of compatible models for a local assistant, document search and speech processing.

Game consoles Possible acceleration of suitable models for speech, character dialogue and other neural features, in integration with the platform maker and game developers.

Servers Inference for compatible neural network models, assessed by throughput, latency and energy of the whole system.

How the direction develops

Run a selected trained model end to end and check the quality of its results.
Build a prototype with memory and all required operations; measure speed and energy.
Connection interface, driver and runtime for the target platform.
Scale up where the benefit is confirmed, and test in a partner's real scenario.

Discuss the AI accelerator direction

Where we are

Development stage

We are developing an experimental prototype and preparing hardware testing. Four research layouts are built and checked in an open toolchain; the compact neural core τ is at the concept stage.

Next step: hardware testing of the prototype, then measurement of power and timing.

Concept visualization

Ternara Φ

What it explores: A reference answer for checking start-up and the digital interface.
Where it can help: Debugging electronics, test benches, training.
Stage: Layout · simulation in CI

Concept visualization

Ternara e

What it explores: Experimental matrix compute for future analysis of sensor data.
Where it can help: After further work: telling normal from unusual states of a fan, compressor or pump.
Stage: Research layout · simulation in CI

Concept visualization

Ternara γ

What it explores: Event-driven neural compute for researching equipment signals.
Where it can help: After further work: unusual vibration patterns, repeating pulses, mode changes.
Stage: Research layout · simulation in CI

Concept visualization

Ternara Corona

What it explores: Reference number formats for checking compute systems.
Where it can help: Engineering tools: testing decoders, cross-checking software and hardware results.
Stage: Research layout; manufacturing of this version is not planned

Concept visualization

Ternara τ

What it explores: We are developing a compact core for local neural processing in home devices.
Where it can help: Classifying prepared sensor signals in appliances. Future: AI accelerator →
Stage: Concept

A device does not need all five. Φ and Corona serve engineering verification; e, γ and τ are different research approaches, chosen for the task.

Full technical details: tests, known limitations, evidence →

Pre-order

Pre-order Ternara chips.

Reserve prototype chips now. We confirm each reservation by email; dates, quantities and terms are confirmed after hardware testing of the prototype. No payment is taken at this stage.

Prototype on an evaluation board For engineers and developers: a prototype chip on a board for your own tests.

Pilot batch For device makers: a reservation of prototype chips for a pilot in your product.

In your email, tell us:

Name and company
Country
Option and quantity
What you plan to use it for

Pre-order by email

What happens next: we reply by email to confirm the reservation, and write again when dates and terms are known.

Pilot

Let's build your device scenario together.

A pilot is a short joint project around one device scenario, for example climate control. We agree on the goal, the data and the success criteria, and show the result in an environment named up front: a software model, a simulation or a hardware bench.

Choose one scenario and agree which part Ternara does and which part your components and device do.
Define the input data, the permitted commands and the success criteria.
Prepare a demonstrator and a test report in the environment that is actually available.
Agree on the next stage after the results.

In your email, tell us:

Company and device
The scenario you want
Data and signals available
Contact person

Discuss a pilot

Next comes a short call to choose the scenario and the success criteria. Supply terms are discussed after the pilot results.

Development directions

From the home to autonomous systems.

The same local processing can serve equipment far from a reliable connection. Each direction starts with a pilot on real data.

Pumps and water supply Local analysis of vibration, current or other available signals; spotting deviations in operation.

Pilot direction

Agriculture Processing sensor readings for irrigation, greenhouses and remote sites.

Pilot direction

Industry Monitoring equipment condition and classifying events on site.

Pilot direction

Aviation Researching local processing of diagnostic sensor data.

Research direction

Space systems Researching compact compute for autonomous telemetry processing.

Research direction

Company

Ternara: intelligence where there is no connection.

We are developing compute cores for compact devices that process sensor data locally. Our goal is to help monitor equipment, agriculture and remote sites of any kind with Ternara AI, without a constant connection to the cloud.

For investors

Nearest verifiable result: Hardware testing of the prototype with measured power and timing.
Possible path to revenue: Paid pilots with device makers, then a joint prototype in a partner's device, then licensing of the cores or supply of chips under an agreement.

Team

Dmitrii Fedorov Founder

dmitrii@ternara.net