Something about us right at the end
Page 40:
The Role of
Neuromorphic Chips
Neuromorphic chips represent an
emerging technology that is designed
to mimic the human brain’s neural
architecture. These chips are inherently
efficient at processing sensory data
in real time due to their event-driven
nature. Therefore, they hold promise
to advance edge AI based on a new
wave of low-power solutions that will
be handling complex tasks like pattern
recognition or anomaly detection.
In the next few years, neuromorphic
chips will become embedded in
smartphones, enabling real-time AI
capabilities without relying on the
cloud. This will allow tasks like speech
recognition, image processing, and
adaptive learning to be performed
locally on these devices with minimal
power consumption. Companies
like Intel and IBM are advancing
neuromorphic chip designs (e.g., Loihi
2 [71] and TrueNorth [72] , respectively)
that consume 15–300 times less
energy than traditional chips while at
the same time delivering exceptional
performance.
WTF!!!
Page 64:
Neuromorphic Chips + 6G +
Quantum Computing
The long-term trajectory of
neuromorphic computing extends
beyond existing edge AI systems. The
integration of neuromorphic AI with 6G
networks and quantum computing is
expected to enable ultra-low-latency,
massively parallel processing at the
edge. BrainChip’s Akida processor and
state-space models, such as Temporal
Event-Based Neural Networks
(TENNs), are early indicators of this
direction, demonstrating the feasibility
of lightweight, event-driven AI
architectures for high-speed, real-time
applications [120] .
As scaling challenges are addressed,
neuromorphic chips will move from
niche applications to mainstream
adoption, powering the next
generation of autonomous machines,
decentralized AI, and real-time
adaptive systems. The future of edge
AI will depend on how efficiently
intelligence is deployed. Neuromorphic
computing is positioned to make that
shift possible.
Whole page 65 is about Brainchip:
EDGE AI INSIGHTS: Brainchip
65
New Approaches for GenAI
Innovation at the Edge
Recent advances in LLM training algorithms, such as
Deepseek’s release of V3, have rattled the traditional AI
marketplace and challenged the belief that large language
models require high computational investments to achieve
performant results. This demonstrates that trying a new
approach can have a big impact on a key challenge the
AI market faces: high computational power and resultant
costs to train and execute LLM models. New approaches
must be considered to address similar challenges at the
edge, such as the large compute and memory bandwidth
requirements of transformer-based GenAI models that
result in on costly and power-hungry edge AI devices.
State Space Models:
More Efficient than Transformers
State Space Models (SSMs), with high-performance models
like Mamba, have emerged in the last two years in cloud-
based LLM applications to address the high computational
complexity and power required to execute transformers in
data centers. Now, there is growing interest in using SSMs
to implement LLMs at the edge and replace transformers,
as they can achieve comparable performance with fewer
parameters and less overall complexity.
Like transformers, SSMs can process long sequences
(context windows). However, their complexity is on the
order of the sequence length O(L), compared to the order of
the square of the sequence length O(L 2 ) for transformers—
and with 1/3 as many parameters. Not to mention that SSM
implementations are less costly and require less energy.
These models leverage efficient algorithms that deliver
comparable or even superior performance. Brainchip’s
unique approach is to constrain an SSM model to better
fit physical time series or streaming data and achieve
higher model accuracy and efficiency. BrainChip innovation
of SSM models constrained to streaming data are called
Temporal Enabled Neural Networks, or TENNs. Combined
with optimized LLM training, they pave the way for a new
category of price and performance LLM and VLM solutions
at the edge.
Deploying LLMs at the Edge:
Efficiency and Scalability
BrainChip addresses the challenge of deploying LLMs at
the edge by using SSMs that minimize computations, model
size, and memory bandwidth while producing state-of-the-
art (SOTA) accuracy and performance results to support
applications like real-time translation, contextual voice
commands, and complete LLM models with RAG extensions.
Brainchip can condense the software model and the
implementation into a tiny hardware design. A specialized
LLM design can execute the edge LLM execution in under
a watt and for a few dollars using a dedicated IP core that
can be integrated into the customer’s SoCs. This enables a
whole new class of consumer products that do not require
costly cloud connectivity and services.
This ultra low power execution makes edge LLMs viable
for always-on devices like smart assistants and wearables.
Cloud LLM services are neither private nor personalized. A
completely local edge AI design enables real-time GenAI
capabilities without compromising privacy, ensuring users
have greater control over their data and enabling a new
class of personalization you can bring wherever you go.
Emerging designs like BrainChip’s Akida core offer a
scalable and efficient solution for engineers and product
developers who want to integrate advanced AI capabilities
into private, personalized consumer products, including
home, mobile, and wearable products.
I might have missed something, it was a quick skim over!
BrainChip is one of the many sponsors, therefore, mentioned in the end.
Position 1: Full-Custom Digital ASIC Design for Edge AI Design energy-efficient chips for real-time AI in IoT (
