Accuracy of Digital | Efficiency of Analog
Meet the industry’s newest architecture for AI computing on-the-device and at-the-edge. DigAn™
by Ambient Scientific is the foundational technology suite for a new generation of AI at the
Solving the three primary bottlenecks of on-device AI requires reinventing conventional computing
architecture. Ambient Scientific built its DigAnTM architecture from the ground up. Taking the best
aspects of digital and analog computing for AI applications, the DigAnTM architecture is optimized for
inference and training with neural network models. You can create new business models, products, and
scalable IP with the DigAnTM technology suite by Ambient Scientific.
DigAnTM is Designed for AI Workloads
The DigAnTM architecture is designed specifically for AI applications. We’ve overcome the
obstacles to creating scalable, low-cost, low-power AI hardware.
- Low power: 10x lower power consumption than best-in-class industry solutions (~8µW per
- Scalable: Manufactured with CMOS process to scale from 40nm to 7nm and beyond.
- High performance compute: 256 MAC per AI core per clock cycle @ 40nm node; scales to 2048
MAC @ 7nm node.
- Programmable operand resolution: Developer can select 4-bit to 32-bit resolution to optimize
power consumption for any AI model.
- Dynamically change AI model resolution: Operand resolution can be selected in the
application and changed at runtime.
Optimized for On-device Inference and Training
- High MAC count: The DigAnTM matrix compute-engine in our AI core is optimized to quickly
execute MAC operations in neural networks.
- On-device weights and activation functions: All neural network parameters are
software-defined and programmable.
- Real-time AI: DigAnTM architecture enables on-device inference and training in real time.
- Character Morphing: Dynamically switch between inference and training based on application
- Highly integrated: The DigAnTM architecture integrates an ARM M4F, multi-channel ADC,
oscillator, and more to reduce system complexity and total BOM cost.
- Sensor fusion: Use onboard I/Os to acquire data from multiple analog or digital sensors and
sensor elements simultaneously.
- Always-ON AI pipeline: Ingest sensor data for wake-on-event and anomaly detection, even when
the onboard processor is in power-down mode.