This blog post was originally published at BrainChip’s website. It is reprinted here with the permission of BrainChip.
According to Gartner, traditional computing technologies will hit a digital wall in 2025 and force a shift to new strategies, including those involving neuromorphic computing. With neuromorphic computing, endpoints can create a truly intelligent edge by efficiently identifying, extracting, analyzing, and inferring only the most meaningful data. Untethered from the cloud, neuromorphic edge AI silicon is already enabling people to seamlessly interact with smarter devices that independently learn new skills, intelligently anticipate requests, and instantly deliver services.
Unlocking the full potential of edge AI with BrainChip
At BrainChip, we believe edge AI presents both a challenge and opportunity for the semiconductor industry. Specific strategies to unlocking the full potential of edge AI will undoubtedly vary, which is why we are launching a new company blog series to explore how neuromorphic edge silicon can mimic the human brain to analyze only essential sensor inputs at the point of acquisition.
We’ll take an in-depth look at the primary design principles of neuromorphic edge silicon, discuss scaling and optimizing on-chip memory, review key strategies for efficiently leveraging incremental and one-shot learning, and detail how to write more efficient machine learning models. We’ll also highlight real world edge AI use cases powered by BrainChip’s Akida neural networking processor, including medical sensors, automotive edge learning at high speeds, object detection and classification, and keyword spotting.
The future’s not only bright, it’s essential
In recent years, neuromorphic computing has enabled new learning models and architectures for edge AI. Smart edge silicon that follows the principles of essential AI—doing more with less—now supports a new generation of advanced multimodal use cases with independent learning and inference capabilities, faster response times, and a lower power budget. By keeping machine learning on the device, neuromorphic edge silicon dramatically reduces latency, minimizes power consumption, and improves security.
We are excited to launch our new company blog series to explore how neuromorphic computing supports the unique learning and performance requirements of edge AI. We look forward to provoking conversation and collaboration as we deploy effective edge compute across real-world applications such as connected cars, consumer electronics, industrial and commercial IoT, and other areas.
