Feed: AnandTech
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Title: Intel and Sandia National Labs Roll Out 1.15B Neuron ΓÇ£Hala PointΓÇ¥
Neuromorphic Research System

Date: Wed, 17 Apr 2024 11:00:00 EDT
Link: https://www.anandtech.com/show/21355/intel-an...

While neuromorphic computing remains under research for the time being,
efforts into the field have continued to grow over the years, as have the
capabilities of the specialty chips that have been developed for this
research. Following those lines, this morning Intel and Sandia National
Laboratories are celebrating the deployment of the Hala Point neuromorphic
system, which the two believe is the highest capacity system in the world.
With 1.15 billion neurons overall, Hala Point is the largest deployment yet
for Intel's Loihi 2 neuromorphic chip, which was first announced at the tail-
end of 2021.

The Hala Point system incorporates 1152 Loihi 2 processors, each of which is
capable of simulating a million neurons. As noted back at the time of Loihi
2's launch, these chips are actually rather small - just 31 mm2 per chip with
2.3 billion transistors each, as they're built on the Intel 4 process (one of
the only other Intel chips to do so, besides Meteor Lake). As a result, the
complete system is similarly petite, taking up just 6 rack units of space (or
as Sandia likes to compare it to, about the size of a microwave), with a
power consumption of 2.6 kW. Now that it's online, Hala Point has dethroned
the SpiNNaker system as the largest disclosed neuromorphic system, offering
admittedly just a slightly larger number of neurons at less than 3% of the
100 kW British system.

A Single Loihi 2 Chip (31 mm2)

Hala Point will be replacing an older Intel neuromorphic system at Sandia,
Pohoiki Springs, which is based on Intel's first-generation Loihi chips. By
comparison, Hala Point offers ten-times as many neurons, and upwards of 12x
the performance overall,

Both neuromorphic systems have been procured by Sandia in order to advance
the national lab's research into neuromorphic computing, a computing paradigm
that behaves like a brain. The central thought (if you'll excuse the pun) is
that by mimicking the wetware writing this article, neuromorphic chips can be
used to solve problems that conventional processors cannot solve today, and
that they can do so more efficiently as well.

Sandia, for its part, has said that it will be using the system to look at
large-scale neuromorphic computing, with work operating on a scale well
beyond Pohoiki Springs. With Hala Point offering a simulated neuron count
very roughly on the level of complexity of an owl brain, the lab believes
that a larger-scale system will finally enable them to properly exploit the
properties of neuromorphic computing to solve real problems in fields such as
device physics, computer architecture, computer science and informatics,
moving well beyond the simple demonstrations initially achieved at a smaller
scale.

One new focus from the lab, which in turn has caught Intel's attention, is
the applicability of neuromorphic computing towards AI inference. Because the
neural networks themselves behind the current wave of AI systems are
attempting to emulate the human brain, in a sense, there is an obvious degree
of synergy with the brain-mimicking neuromorphic chips, even if the
algorithms differ in some key respects. Still, with energy efficiency being
one of the major benefits of neuromorphic computing, it's pushed Intel to
look into the matter further - and even build a second, Hala Point-sized
system of their own.

According to Intel, in their research on Hala Point, the system has reached
efficiencies as high as 15 TOPS-per-Watt at 8-bit precision, albeit while
using 10:1 sparsity, making it more than competitive with current-generation
commercial chips. As an added bonus to that efficiency, the neuromorphic
systems don't require extensive data processing and batching in advance,
which is normally necessary to make efficient use of the high density ALU
arrays in GPUs and GPU-like processors.

Perhaps the most interesting use case of all, however, is the potential for
being able to use neuromorphic computing to enable augmenting neural networks
with additional data on the fly. The idea behind this being to avoid re-
training, as current LLMs require, which is extremely costly due to the
extensive computing resources required. In essence, this is taking another
page from how brains operate, allowing for continuous learning and dataset
augmentation.

But for the moment, at least, this remains a subject of academic study.
Eventually, Intel and Sandia want systems like Hala Point to lead to the
development of commercial systems - and presumably, at even larger scales.
But to get there, researchers at Sandia and elsewhere will first need to use
the current crop of systems to better refine their algorithms, as well as
better figure out how to map larger workloads to this style of computing in
order to prove their utility at larger scales.CP

Gallery: Intel and Sandia National Laboratories Roll Out ΓÇ£Hala PointΓÇ¥
Neuromorphic Research System

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