All-analog Neural Signal Processor Technology
Empowering Innovations with Blumind's Proprietary Architecture
Unlock the true potential of analog AI compute with Blumind's cutting-edge semiconductor architecture, combining machine learning, precision analog signal processing, and brain-inspired computing.
AMPL™ All-analog
Compute Advantages
Blumind delivers high performance all-analog compute solutions for edge AI using CMOS technology on advanced process nodes. No special processing steps, no specialty memory structures, no technology risks.
Analog AI Innovation
Blumind AMPL™ is a disruptive analog AI compute fabric for micropower artificial intelligence applications.
Precision and Accuracy
Blumind all-analog AI compute delivers deterministic and precise inferencing performance at up to x1000 lower power than our competitors. Delivering higher efficiency and the longest battery life for always-on applications.
Low Latency Solutions
AMPL™ fabric delivers efficient low latency for real-time applications.
Analog Breakthrough
AMPL™ is the first all-analog AI on advanced standard CMOS architected to fundamentally mitigate process, voltage, temperature and drift variations.
Watch the video webinar "Why all-analog compute is vital for edge based neural network processing."
Content opens in a new window. A full screen hi-res version of the video can be found HERE.
Blumind’s All-analog Difference
Blumind's AMPL™ core technology is architected to be all-analog from the ground up. Unlike other analog edge AI solutions AMPL™ is efficient, robust and uses CMOS technology on advanced process nodes.
Direct Analog Sensor Input
Standard CMOS Process
Advanced Process Node
Low Latency
Analog Precision
Ultra-low Power
No ADC & DAC
No SRAM
No High Speed Clock
No Added Processing Steps
No Novel Process Technology Modules
Supports Sustainable Solutions
Key Features Unique Technology
Software and Solutions
The Blumind AMPL™ core used for inferencing takes parameters from
industry standard software flows like PyTorch and TensorFlow
industry standard software flows like PyTorch and TensorFlow