Global High-Speed Neuromorphic Computing Chips Market to Reach US$ 8.0 Billion by 2031 at 27.3% CAGR, Driven by Intel and IBM Leadership

In 2025, the global High-Speed Neuromorphic Computing Chips market continues its rapid growth trajectory, driven by breakthroughs in scalable architectures, energy efficiency, and edge deployment. According to leading industry projections from QYResearch, the market is poised to grow from US$ 1,481 million in 2024 to US$ 8,023 million by 2031, representing a compound annual growth rate (CAGR) of 27.3 % from 2025 to 2031. In 2024 alone, approximately 14.5 million units were produced globally, with an average selling price of about US$ 102 per unit. Typical single-line manufacturing capacity is estimated at around 1,000,000 units per year.

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The gross margin is 58.6 %, the implied COGS for the category in 2024 would be ~US$ 613 million, yielding an implied gross profit of ~US$ 868 million.
Below is a structured breakdown of market drivers, new product and research revelations, architectural trends, and the competitive ecosystem.

Leading Companies
Intel
IBM
Qualcomm
Samsung Electronics
NVIDIA
SynSense
BrainChip Holdings
SK hynix
Analog Devices
Infineon Technologies
Micron Technology
Samsung Advanced Institute of Technology
TSMC
SMIC
Renesas Electronics
STMicroelectronics
ARM Holdings
Graphcore
Tenstorrent
Prophesee
GrAI Matter Labs
Mythic AI
Numenta

Application Segments
• Intelligent Robotics
• Autonomous Driving Systems
• Edge AI Devices
• Medical Neural Network Computing
• Others

Classification / Chip Type Segments
• Synaptic Transistor-Based Chips
• Spiking Neural Network-Based Chips

Recent Highlights & 2025 Advances
• Intel's Hala Point system has hit the spotlight as the world's largest neuromorphic research deployment. The system comprises 1,152 Loihi 2 processors, offering 1.15 billion neurons and 128 billion synapses, organized across 140,544 neuromorphic cores, all consuming up to 2,600 watts in a compact rack setup. It achieves peak computational throughput equivalent to 20 peta-operations per second with >15 TOPS/W efficiency on 8-bit workloads. This system marks a large-scale testbed for spiking neural networks and hybrid DNN workloads under real constraints.
• IBM's NorthPole prototype is demonstrating compelling inference performance. In experiments with a 3-billion-parameter LLM, the NorthPole system attained latency under 1 ms per token and throughput of 28,356 tokens/s, while delivering ~72.7× better energy efficiency than the next most efficient GPU in the comparison. The chip is fabricated at 12 nm, contains 22 billion transistors, and occupies about 795 mm2.
• In neural modeling, researchers have successfully mapped the Drosophila melanogaster whole-brain connectome (140k neurons, 50 M synapses) onto Loihi 2 across 12 chips, running much faster than traditional simulators. This demonstrates increasingly "biological-scale" modeling becomes feasible on neuromorphic platforms.
• On the algorithmic front, new conversion methods leveraging graded spikes on Loihi 2 enable conversion of conventional neural networks to spiking equivalents without excessive time steps. Applied models have reported up to 3× throughput improvement and 2× energy savings relative to edge GPU baselines for small transformer-style models.
• Event-driven processing is becoming more practically convincing: vision pipelines like "robust fitting" have been mapped to Loihi 2 with energy consumption as low as 15 % of equivalent CPU implementations. This offers a compelling case for deploying neuromorphic modules in real-time perception.
These advances collectively shift neuromorphic computing from niche demonstrations toward early commercial pilots, especially where low latency, energy constraints, and dynamic adaptation matter (edge AI, robotics, sensing).

Product Highlights from Five Leading Firms
Intel (Loihi 2 / Hala Point System)
• Loihi 2 supports on-chip learning, programmable spiking dynamics, modular connectivity, and graded spikes.
• In Hala Point deployment, ~1.15 B neurons / 128 B synapses, 1,152 chips in a compact rack form factor, 2,600 W envelope.
• Demonstrated >15 TOPS/W on sparse DNN workloads and 20 peta-ops system throughput.

IBM (NorthPole Accelerator)
• 12 nm process, ~795 mm2 die, ~22 billion transistors.
• Achieves

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