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The guide to understanding the state of the art in hardware & software in Edge AI.
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From the report (Chapter X):
Neuromorphic Computing: Increase AI Intelligence by Mimicking the Human Brain
The future of Edge AI will also be shaped by novel AI paradigms such as neuromorphic computing. This approach mimics the human brain's structure and functionality by emulating the neural networks and synaptic connections in our brains. It is based on novel neuromorphic chips that process information more efficiently while adapting faster and more effectively to new situations. In practice, neuromorphic chips comprise many artificial neurons and artificial synapses that can mimic the functioning of brain spikes. Therefore, neuromorphic computing research brings us a step closer to understanding, decoding, and exploiting the code of the human brain in AI applications.
Neuromorphic computing chips are well-suited to deliver Edge AI benefits at scale. This is because they consume less power and provide faster processing speeds than conventional processors. Most importantly, they are equipping Edge AI systems with human-brain-like reasoning capabilities that will be extremely useful in many pervasive applications (e.g., obstacle avoidance, robust acoustic perception, etc.). As neuromorphic computing technology matures, it will enable a new generation of AI-based edge devices that can learn and adapt in real-time.
Event-based Processing & Learning: BrainChip’s Neuromorphic AI Solution
BrainChip is one of the pioneers of bringing neuromorphic computing to the edge. While traditional neuromorphic approaches have used analog designs to mimic the neuron and synapse, BrainChip has taken a novel approach on three counts.
- Firstly, their design is a fully digital design that is portable and reliable.
- Secondly, not only do they support spiking neural nets, but they have applied event-based execution to traditional convolutional networks, thereby rendering neuromorphic computing mainstream today. This allows current CNN/RNN models to run much more efficiently and drives far more capable performance on extremely low-footprint, low-power devices at the sensor.
- Thirdly, delivering on-device learning allows for personalization, customization, and other learning untethered from the cloud.
Brainchip’s Akida neural processor is offered as IP and is configurable from energy-harvesting applications at the sensor edge to high-performance yet power-efficient solutions at the network edge. It is sensor-agnostic and has been demonstrated on a variety of sensors.
As a self-managed neural processor that executes most networks completely in hardware without CPU intervention, it addresses key congestion and system bandwidth challenges in embedded SoCs while delivering highly efficient performance. With support for INT8 down to INT1 and skip connections, it handles most complex networks today, along with spiking neural nets.
This led NASA to select BrainChip’s first silicon platform in 2021 to demonstrate
in-space autonomy and cognition in one of the most extreme power- and thermally-constrained applications. Similarly, Mercedes Benz demonstrated BrainChip in their EQXX concept vehicle that can go over 1000 km on a single charge.
In the latest generation, Brainchip has taken another big step of adding Temporal Event Based Neural Nets (TENNs) and complementary separable 3D convolutions that speed up some complex time-series data applications by 500x while radically reducing model size and footprint, but without compromising accuracy. This enables a new class of compact, cost-effective devices to support high-res video object detection, security/surveillance, audio, health, and industrial applications.
While neuromorphic computing is still discussed as a future paradigm, BrainChip is already bringing this paradigm to market.
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The new guide from Wevolver offers a roadmap to edge AI success filled with insights and case studies from companies like Arm, Edge Impulse, BrainChip , Arduino and more.
