Hi. My name is Altaf Khan. I am the CEO of Infxl. Infxl develops tiny, low-latency, hardware-agnostic AI for tabular and time-series data.
On a Cortex-M4, our inference engine occupies 110 bytes. On a 200 MHz FPGA, it has a latency of 35-55 ns. You can also deploy it on any other 8/16/32/64-bit MCU, DSP, or FPGA.
Applications include IoT edge devices, predictive maintenance, healthcare/fitness wearables, high-frequency trading, and many more.
If it interests you, let us set up a quick call. Otherwise, please point me to those in your network who may find it beneficial.
You may visit removed link to test-drive our solution.
Have a great day!
Altaf
Thanks @altaf for sharing your company's solution. To gain better visibility - do consider submiting an actual deployed AI use case, preferably in Singapore.
Cheers!
Outcompute to outcompete | Growing our own timber
@altaf - your first post included a link, which the forum system automatically removes (As part of SPAM control). With your FIRST post now approved, you can post links to your website, and product pages etc.
Cheers!
Outcompute to outcompete | Growing our own timber
USE CASE #1: IoT Condition Monitoring
Infer the state of a fan based on instantaneous sensor data
STATES: Normal; Low voltage; Stuck object; Obstructed
SENSORS: Triaxial Accelerometer; PWM; Tachometer
FPGA-BASED INFERENCE:
240 LUT, 340 FF, 0 DSP on a Microchip PolarFire® 200 MHz FPGA
Structure/parameters updateable without pausing the operation
96% accuracy with 4,000,004 samples
57 nJ/inference
18 μs/inference for the sequential implementation of Infxl inference engine
USE CASE #2: Particle Identification (High-Energy Physics)
Identify, in real-time, events of interest in proton-proton collisions at the CERN Large Hadron Collider
TASK: Distinguish between five elementary particles
FEATURES: 16 Physics-motivated, high-level features crafted by experts
FPGA-BASED INFERENCE:
35 ns/inference for the parallel implementation of the Infxl inference engine on a 200 MHz FPGA
The solution does not use DSP blocks
72.1% accuracy with 830,000 samples
USE CASE #3: Battery-less 60x45x2 mm Card for Gesture Recognition
Recognize gestures performed using a credit-card-like battery-less device
TASK: Recognize up-down and side-ways movement gestures
SENSOR: Triaxial accelerometer
MCU-BASED INFERENCE:
MCU-based card operates solely on the energy from the solar cell that covers one side of the card
94% accurate recognition
REFERENCE:
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