拉丁美洲的神經型態晶片：市場佔有率分析、行業趨勢和成長預測（2025-2030）

LA Neuromorphic Chip - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)

出版日期: 2025年01月05日 | 出版商:

Mordor Intelligence | 英文 120 Pages | 商品交期: 2-3個工作天內

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簡介目錄

拉丁美洲神經型態晶片市場預計在預測期內複合年成長率為27.89%

主要亮點

神經型態是一種受大腦啟發的 ASIC，它實現了尖峰神經網路 (SNN)。平均數十瓦即可實現大規模平行大腦處理能力。記憶體和處理單元是一個單一的抽象（記憶體內運算）。這帶來了在複雜環境下動態和自可程式設計行為的優勢。
BrainChip Holdings Ltd. 等公司已經建立了多個夥伴關係關係，利用神經型態晶片來遏制 COVID-19 的傳播。 2021 年 5 月，BrainChip Holdings Ltd 與精密免疫學公司 Biotome Pty Ltd 合作開發了一種快速、準確的 COVID-19 抗體測試。兩家公司將探索 Akida 神經處理器如何提高抗體測試中資訊的準確性和質量，而 Biotome 將透過在照護端提供先進的人工智慧功能來推進開發。
神經型態晶片可以設計為數位晶片、類比晶片或兩者的混合晶片。與數位晶片相比，類比晶片更類似於神經網路的生物特性。在模擬架構中，使用較少的電晶體來模擬神經元的微分方程。因此，理論上它比數位神經型態晶片消耗更少的能量。此外，處理可以擴展到超出分配的時隙。此功能允許比即時更快地執行處理。然而，模擬架構噪音更大且精度較低。
另一方面，數位晶片比類比晶片具有更高的精度。其數位結構增強了片上程式設計。這種靈活性使得人工智慧研究人員能夠以比 GPU 更低的功耗準確地實現各種演算法。混合晶片試圖將類比晶片的低能耗與數位晶片的高精度結合。
神經型態架構解決了馮諾依曼架構中常見的挑戰，例如高功耗、低速度和其他效率瓶頸。與二元編碼中具有突然高低差異的傳統馮諾依曼架構不同，神經型態晶片以尖峰訊號的形式提供連續的類比轉換。神經型態架構將儲存和處理融為一體，消除了CPU和記憶體之間的匯流排瓶頸。

拉丁美洲神經型態晶片市場趨勢

汽車產業是採用神經型態晶片快速發展的產業

汽車產業是神經型態晶片成長最快的產業之一。所有高階汽車製造商都在大力投資，以在其車輛中實現 5 級自治，預計這將導致對人工智慧驅動的神經型態晶片的巨大需求。
自動駕駛市場需要不斷改進人工智慧演算法，以低功耗實現高吞吐量。神經型態晶片非常適合分類任務，可用於自動駕駛的多種場景。與靜態深度學習解決方案相比，即使在自動駕駛汽車等嘈雜的環境中，它也更有效率。
據英特爾稱，自動駕駛汽車在行駛近一個半小時內估計可以產生 4 Terabyte的資料量，相當於普通人一天在汽車上花費的時間。自動駕駛汽車面臨著有效管理這些行程中產生的所有資料的重大挑戰。
為現代自動駕駛汽車提供動力的電腦實際上是微型超級電腦。 NVIDIA 等公司的目標是在 2022 年實現 5 級自治，以 750W 的功率提供 200 TOPS（每秒兆次操作）。然而，每小時花費 750W 的處理能力將對電動車的續航里程產生顯著影響。
ADAS（進階駕駛輔助系統）應用包括神經型態晶片各種車載應用中的影像學習和辨識功能。這與乘用車中的巡航控制和智慧速度輔助系統等傳統 ADAS 功能的工作原理類似。可以透過識別道路上顯示的交通資訊（例如行人穿越道、學區和道路差異）來控制車輛速度。

對基於人工智慧的微晶片的需求不斷成長推動市場成長

由於對人工智慧的需求不斷成長以及消費者對較小產品的偏好導致對 IC 小型化的需求，拉丁美洲神經型態晶片市場正在經歷高速成長。隨著智慧技術的出現，智慧感測器被應用於汽車、電子和醫療等許多最終用戶產業。
目前可用於人工智慧應用的半導體是CPU和人工智慧加速器。由於 CPU 的運算處理能力有限，人工智慧加速器正在引領市場。可用的人工智慧加速器包括 GPU、專用積體電路 (ASIC) 和 FPGA（現場可程式閘陣列）。 GPU擁有許多平行處理核心，這使得它們在處理AI訓練和推理方面具有巨大優勢。然而，GPU由於功耗較高，無法相容於未來的應用。
另一方面，新興的 FPGA 的能源效率比 GPU 高 10 倍，但效能較低。對於能源效率是重中之重的應用，FPGA 可以作為替代解決方案。在AI加速器中，ASIC效能最好，功耗更低，效率更高。然而，設計具有獨特功能的 ASIC 非常昂貴且無法重新配置。因此，當特定人工智慧應用的市場適合設計投資時，應該使用 ASIC。
與人工智慧加速器相比，神經型態晶片在並行性、能源效率和性能方面可能是更好的選擇。神經型態晶片可以即時處理人工智慧推理和訓練。此外，還可以透過神經型態晶片進行邊緣訓練。但學習方法的準確性仍有待提升。

拉丁美洲神經型態晶片產業概況

由於神經型態晶片市場非常小眾，且處於市場發展的早期階段，市場上只有BrainChip Holdings Ltd、英特爾公司和SynSense AG等少數公司。主要企業透過協作、市場開發、產品創新和研發活動等各種市場開發策略，在這個一體化的市場場景中蓬勃發展。因此，市場集中度適中。

2020 年 3 月 - SolidRun 和 Gyrfalcon 開發了首款邊緣最佳化 AI 推理伺服器 Janux GS31，支援主要的神經網路框架。最多可安裝128顆Gyrfalcon Lightspeeur SPR2803 AI加速晶片，以提高最複雜的視訊AI模型的推理性能。

其他好處：

Excel 格式的市場預測 (ME) 表
3 個月分析師支持

Key Highlights

Neuromorphic is a specific brain-inspired ASIC that implements the Spiked Neural Networks (SNNs). It has an object to reach the massively parallel brain processing ability in tens of watts on average. The memory and the processing units are in single abstraction (in-memory computing). This leads to the advantage of dynamic, self-programmable behavior in complex environments.
Companies, such as BrainChip Holdings Ltd, are forming multiple partnership activities to utilize neuromorphic chips in curbing the spread of COVID-19. In May 2021, BrainChip Holdings Ltd partnered with precision immunology company Biotome Pty Ltd to develop a fast, accurate COVID-19 antibody test. The companies will explore how the Akida neural processor could improve the accuracy and information quality of the antibody tests while Biotome is developing by providing advanced AI capacity at the point of care.
Neuromorphic chips can be designed digitally, analog, or in a mixed way. Analog chips resemble the characteristics of the biological properties of neural networks better than digital ones. In the analog architecture, few transistors are used for emulating the differential equations of neurons. Therefore, theoretically, they consume lesser energy than digital neuromorphic chips. Besides, they can extend the processing beyond its allocated time slot. Thanks to this feature, the speed can be accelerated to process faster than in real-time. However, the analog architecture leads to higher noise, which lowers the precision.
Digital ones, on the other hand, are more precise compared to analog chips. Their digital structure enhances on-chip programming. This flexibility allows artificial intelligent researchers to accurately implement various kinds of an algorithm with low-energy consumption compared to GPUs. Mixed chips try to combine the advantages of analog chips, i.e., lesser energy consumption, and the benefits of digital ones, i.e., precision.
Neuromorphic architectures address challenges, such as high-power consumption, low speed, and other efficiency-related bottlenecks prevalent in the von Neumann architecture. Unlike the traditional von Neumann architecture with sudden highs and lows in binary encoding, neuromorphic chips provide a continuous analog transition in the form of spiking signals. Neuromorphic architectures integrate storage and processing, getting rid of the bus bottleneck connecting the CPU and memory.

Latin America Neuromorphic Chip Market Trends

Automotive is the Fastest Growing Industry to Adapt Neuromorphic Chip

The automotive industry is one of the fastest-growing industries for neuromorphic chips. All the premium car manufacturers are investing heavily to achieve Level 5 of Vehicle Autonomy, which in turn, is anticipated to generate huge demand for AI-powered neuromorphic chips.
The autonomous driving market requires constant improvement in AI algorithms for high throughput with low power requirements. Neuromorphic chips are ideal for classification tasks and could be utilized for several scenarios in autonomous driving. Compared with static deep learning solutions, they are also more efficient in a noisy environment, such as self-driving vehicles.
According to Intel, four terabytes is the estimated amount of data that an autonomous car may generate through almost an hour and a half of driving or the amount of time a general person spends in their car each day. Autonomous vehicles face a significant challenge in efficiently managing all the data generated during these trips.
The computers running the latest self-driving cars are effectively small supercomputers. The companies, such as Nvidia, aim to achieve Level 5 autonomous driving in 2022, delivering 200TOPS (trillions of operations per second) using 750W of power. However, spending 750W an hour on processing is poised to have a noticeable impact on the driving range of electric vehicles.
ADAS (Advanced Driver Assistance System) applications include image learning and recognition functions among various automotive applications of neuromorphic chips. It works like conventional ADAS functions, such as cruise control or intelligent speed, assist system in passenger cars. It can control vehicle speed by recognizing the traffic information marked on roads, such as crosswalks, school zone, road-bump, etc.

Increasing Demand for Artificial Intelligence-based Microchips drive the market growth

The Latin American neuromorphic chip market is experiencing high growth due to increasing demand for artificial intelligence and consumer preference towards small-sized products leading to the requirement of miniaturization of ICs. With the advent of smart technologies, smart sensors are being used in many end-user industries like automotive, electronics, and medical.
Currently available semiconductors for AI applications are CPUs and AI accelerators. The AI accelerators are leading the market because of the computing limitations of CPUs. Available AI accelerators are GPUs, Application-Specific Integrated Circuits (ASICs), and Field-Programmable Gate Arrays (FPGAs). GPUs have many parallel processing cores, which give them a significant advantage for processing AI training and inference. However, they do have a high-power consumption cost which is not sustainable for future applications.
On the other hand, emerging FPGAs can have ten times more power efficiency than GPUs but have lower performance. In applications where energy efficiency is the top priority, FPGAs can be the alternative solution. Among AI Accelerators, ASICs show the best performance, lesser power consumption, and efficiency. However, designing unique functioning ASIC is highly costly and is not reconfigurable. Therefore, ASICs should be used when the market of specific AI applications is adequate for the design investment.
Compared to AI Accelerators, neuromorphic chips are poised to be the prominent option concerning parallelism, energy efficiency, and performance. They can handle both AI inference and training in real-time. Moreover, edge training is possible through neuromorphic chips. However, learning methodologies should be improved their accuracy.

Latin America Neuromorphic Chip Industry Overview

As the market for neuromorphic chips is very niche and in the initial phase of development, the market has a presence of a few players, such as BrainChip Holdings Ltd, Intel Corporation, SynSense AG, etc. Top players are growing intensely in this consolidated market scenario through various market development strategies, such as collaboration, market expansion, product innovation, and R&D activities. Hence the market concentration is medium.

March 2020 - SolidRun and Gyrfalcon developed First Edge Optimized AI Inference Server Janux GS31 that supports leading neural network frameworks. It can be configured with up to 128 Gyrfalcon Lightspeeur SPR2803 AI acceleration chips for improved inference performance for most complex video AI models.

Additional Benefits:

The market estimate (ME) sheet in Excel format
3 months of analyst support

1 INTRODUCTION

1.1 Study Assumptions and Market Definition
1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

4.1 Market Overview
4.2 Industry Attractiveness - Porter's Five Forces Analysis
- 4.2.1 Bargaining Power of Suppliers
- 4.2.2 Bargaining Power of Consumers
- 4.2.3 Threat of New Entrants
- 4.2.4 Threat of Substitutes
- 4.2.5 Intensity of Competitive Rivalry
4.3 Industry Value Chain Analysis
4.4 Emerging Use Cases for Neuromorphic Chips
4.5 Analysis of the Impact of COVID-19 on the Market

5 MARKET INSIGHTS

5.1 Market Drivers
- 5.1.1 Increasing Demand for Artificial Intelligence-based Microchips
- 5.1.2 Emerging Trend of Combining the Concept of Neuroplasticity with Electronics
5.2 Market Challenges
- 5.2.1 Need for High Level of Precision and Complexity in Hardware Design

6 LATIN AMERICA NEUROMORPHIC CHIP MARKET

6.1 End User Industry
- 6.1.1 Financial Services and Cybersecurity
- 6.1.2 Automotive
- 6.1.3 Industrial
- 6.1.4 Consumer Electronics
- 6.1.5 Other End User Industries

7 COMPETITIVE LANDSCAPE

7.1 Company Profiles
- 7.1.1 Intel Corporation
- 7.1.2 SK Hynix Inc.
- 7.1.3 IBM Corporation
- 7.1.4 Samsung Electronics Co. Ltd
- 7.1.5 GrAI Matter Labs
- 7.1.6 Nepes Corporation
- 7.1.7 General Vision Inc.
- 7.1.8 Gyrfalcon Technology Inc.
- 7.1.9 BrainChip Holdings Ltd
- 7.1.10 Vicarious FPC Inc.
- 7.1.11 SynSense AG