Deep Learning Chipset Market by Device Type (ASIC, CPU, FPGA), Deployment Mode (Cloud, Edge, On Premise), End User, Application - Global Forecast 2025-2032
Description
The Deep Learning Chipset Market was valued at USD 11.82 billion in 2024 and is projected to grow to USD 13.70 billion in 2025, with a CAGR of 16.43%, reaching USD 39.96 billion by 2032.
A strategic introduction to deep learning chipsets highlighting core technological enablers, commercialization dynamics, and integration pathways for hardware and software synergy
This executive summary introduces the contemporary deep learning chipset landscape by focusing on technological imperatives, commercial dynamics, and the interaction between hardware and software ecosystems. Advancements in algorithmic complexity and rising inference and training workloads have elevated the role of specialized accelerators, prompting a closer alignment between semiconductor design, system integration, and application requirements. As a result, development roadmaps increasingly prioritize power efficiency per inference, interconnect performance, and software stack maturity to ensure solution-level differentiation.
The narrative that follows situates chipset evolution within broader industry transitions, where heterogeneity and modularity are critical. Developers and systems architects are prioritizing programmability alongside raw throughput, while OEMs and hyperscalers balance capital intensity against deployment flexibility. In this environment, the ability to translate model architecture innovations into sustained hardware advantage depends on robust IP portfolios, efficient production relationships, and a well-defined route to market. By understanding these drivers and constraints, stakeholders can make informed choices about partnership structures, product roadmaps, and integration strategies that align with application-specific demands.
Converging forces reconfiguring compute architectures and data pipelines to accelerate training and inference workloads across heterogeneous accelerators and distributed deployments
The landscape for deep learning chipsets has experienced transformative shifts that reconfigure design priorities, go-to-market approaches, and deployment topologies. Heterogeneous compute stacks have moved from experimental deployments to mainstream adoption, with accelerators designed for specialized matrix operations coexisting alongside general purpose processors. This transition is accompanied by a stronger emphasis on software abstraction layers, enabling models to be portable across ASICs, GPUs, FPGAs and CPUs while preserving performance characteristics.
At the same time, the locus of compute is diversifying: cloud-native training clusters remain critical for large scale model development, but edge and on premise inference deployments are becoming decisive for latency sensitive and privacy constrained applications. Supply chain dynamics have also shifted, pushing firms to localize critical capabilities or identify second source manufacturers to mitigate geopolitical risk. These combined forces are driving a more modular approach to chipset design, with architectures and packaging optimized for thermal envelopes, power budgets, and system-level interoperability rather than single metric supremacy.
Assessment of tariff policy shifts and trade measures affecting supply chains, procurement strategies, and cross border manufacturing for deep learning semiconductor components
Trade measures instituted by the United States and allied policy adjustments through recent years have created persistent implications for semiconductor supply chains, procurement strategies, and manufacturing decisions relevant to deep learning chipsets. Tariff schedules and related export controls have increased the cost and complexity of cross border component flows, influencing decisions about where to locate high value fabrication, assembly, and testing activities. Firms with vertically integrated supply chains or diversified manufacturing footprints are better positioned to absorb such policy driven shifts.
Beyond immediate cost considerations, these trade actions have catalyzed strategic reorientation across ecosystem participants. Original device designers and foundry partners are reassessing long lead time orders, contractual flexibility, and inventory policies to maintain continuity of supply. In addition, equipment vendors and downstream integrators are adapting sourcing strategies to favor regional suppliers and to internalize certain capabilities that were previously outsourced. The cumulative effect has been a reallocation of investment toward supply resilience, alternative sourcing arrangements, and enhanced visibility across multi tier supply networks, all of which influence product roadmaps and time to integration.
In depth segmentation insights revealing device classes, deployment modalities, end user behaviors, and application specific requirements shaping architecture and value chains
Segmentation analysis reveals differentiated drivers and adoption patterns across device types, deployment modes, end users, and application clusters that shape chipset requirements and value propositions. Device distinctions matter: ASICs are being optimized for cost efficient, high density inference; GPUs continue to dominate large scale training and flexible model experimentation; FPGAs retain relevance where reprogrammability and low latency customization are paramount; and CPUs remain essential for control plane tasks and mixed workload orchestration. Each device class imposes distinct constraints on power, latency, and software support, creating clear trade offs for system architects.
Deployment modalities further refine design priorities. Cloud deployments prioritize scalability, multi tenancy and thermal efficient data center packaging, whereas edge implementations emphasize robust thermal management, minimal power draw and local inferencing reliability. On premise deployments often combine the two priorities, requiring enterprise grade security controls and simplified lifecycle management. End user segmentation also delineates expectations: consumer applications demand compact form factors, cost sensitivity and seamless integration into everyday devices, while enterprise customers emphasize uptime guarantees, manageability and compliance with regulatory requirements.
Application verticals place additional, granular constraints on chipset selection and complementary software. Autonomous vehicle systems require fault tolerant compute with specialized ADAS processing and pathways toward fully autonomous stacks, demanding stringent functional safety and deterministic latency. Consumer electronics prioritize energy efficiency and seamless user experiences across smart home devices, smartphones and wearables. Data center priorities split between cloud scale orchestration and on premise enterprise clusters, while healthcare applications emphasize diagnostic accuracy, medical imaging throughput and patient monitoring reliability. Robotics encompasses industrial control precision and service robotics adaptability. Together, this segmentation matrix informs product roadmaps, supplier selection, and co engineering efforts that drive system level differentiation.
Regional dynamics and competitive advantages across the Americas, Europe Middle East and Africa, and Asia Pacific influencing talent, supply and adoption pathways
Regional conditions create distinct competitive advantages, infrastructure profiles, and regulatory contexts that materially influence chipset strategies and adoption pathways. The Americas benefit from a dense concentration of hyperscale cloud providers, leading edge design houses and a mature venture ecosystem that accelerates commercialization cycles and fosters deep talent pools in chip design and systems integration. This environment encourages rapid prototyping, close collaboration between software teams and hardware architects, and early enterprise adoption across cloud centric use cases.
Europe, Middle East & Africa present a heterogeneous set of policy and industrial dynamics, with strong emphasis on data protection, industrial automation, and the integration of semiconductor strategy into broader industrial policy. Regional incentives for localized manufacturing, coupled with regulatory expectations around data sovereignty, drive unique deployment models that favor on premise and hybrid solutions for sensitive applications, including healthcare and critical infrastructure.
Asia Pacific combines vast manufacturing capacity, integrated supply chains, and rapidly growing end user markets that span consumer electronics to industrial automation. The region’s strengths in high volume production, component ecosystem depth, and cross border logistics create favorable conditions for scaling new chipset designs into mass market devices while also providing competitive specialization in packaging, testing and assembly services. Across all regions, talent availability, capital access, and policy signals shape where and how firms prioritize investments along the product life cycle.
Corporate competitive intelligence on chipset vendors, ecosystem partners, and new entrants emphasizing differentiation, partnerships, IP strategies and capital allocation priorities
Competitive dynamics among chipset providers and ecosystem partners are accelerating, with established semiconductor vendors, cloud hyperscalers and specialized AI accelerator startups each pursuing distinct routes to differentiation. Legacy GPU vendors continue to expand software stacks and developer ecosystems to protect broad applicability across training and inference, while custom ASIC efforts focus on vertical specialization and tighter integration with leading model architectures. FPGA based solutions are capitalizing on reprogrammability to target niche latency sensitive and privacy constrained applications where hardware level customization yields measurable benefits.
Partnerships and IP strategies are central to competing effectively in this environment. Strategic alliances between chip designers, foundries, and systems integrators reduce time to integration and provide access to critical manufacturing capacity. New entrants emphasize unique microarchitectures, large scale wafer access agreements, or novel packaging approaches to create defendable positions. Investment in developer tooling, model compilers, and runtime optimization is increasingly as important as raw silicon capability, and firms that cultivate a robust software ecosystem gain sustained commercial advantages. Capital allocation priorities reflect a balance between R&D intensity, go to market scaling, and long term investments in supply chain resiliency.
Actionable strategic recommendations for industry leaders to optimize R D investments, supply resilience, go to market models, and ecosystem orchestration for sustained advantage
Industry leaders should organize strategy around resilient supply chains, prioritized software investments, selective partnerships, and outcome oriented product roadmaps. First, establish multi path sourcing strategies and deepen collaboration with assembly and test partners to reduce single point dependencies. This enhances continuity and enables tactical responsiveness to trade policy adjustments and logistical shocks. Second, invest deliberately in software tooling and compiler optimization that enable model portability across ASIC, GPU, FPGA and CPU targets, thereby reducing integration friction and broadening addressable deployments.
Third, pursue targeted partnerships with hyperscale customers, OEMs and application specialists to co develop solutions that match unique operational constraints and accelerate adoption. Fourth, align product roadmaps with concrete application level metrics-such as latency targets, energy per inference and safety certifications-so that engineering efforts are directly tied to end user outcomes. Finally, embed regulatory and policy monitoring into strategic planning, ensuring that procurement cycles, localization choices and IP protection strategies adapt proactively to evolving trade measures and data governance requirements. These steps position organizations to capture strategic opportunities while maintaining flexibility in an uncertain geopolitical environment.
Transparent research methodology describing primary and secondary approaches, expert interviews, data triangulation, and quality assurance practices underpinning the analysis
The research approach underpinning this analysis relies on a combination of primary expert engagement, targeted technical review, and structured secondary research to ensure robust, reproducible insights. Primary inputs included structured interviews with chipset architects, systems integrators, hyperscaler infrastructure leads, and end user IT decision makers, supplemented by technical briefings with foundry and packaging specialists. These engagements provided granular visibility into design priorities, integration challenges, and procurement considerations across deployment contexts.
Secondary research involved synthesis of publicly available technical papers, regulatory filings, vendor product documentation and open industry standards to validate technical claims and situate strategic trends. Data triangulation methods reconciled divergent perspectives by cross checking supplier roadmaps, end user requirements, and observed deployment patterns. Quality assurance included peer review of analytical assumptions, reproducibility checks on technical performance interpretations, and sensitivity analysis around policy scenarios to ensure conclusions remain robust under plausible conditions. This blended methodology supports a balanced, evidence driven view of chipset dynamics and implications for stakeholders.
Concluding synthesis distilling core findings, strategic implications for stakeholders, and the pragmatic next steps for technology, policy and commercial engagement
In conclusion, the evolution of deep learning chipsets is defined by a convergence of architectural heterogeneity, supply chain reconfiguration, and application specific demands that together shape commercial viability and technical differentiation. The most successful players will couple focused silicon innovation with mature software ecosystems, resilient manufacturing relationships, and targeted partnerships that reduce integration friction for end customers. Strategic clarity around deployment modalities-cloud, edge and on premise-enables organizations to tailor product features to measurable customer outcomes and regulatory constraints.
Policy developments and regional dynamics are now integral to technology strategy, requiring that firms embed geopolitical risk assessment into product planning and supplier selection. By aligning R&D priorities with application led requirements and by investing in developer experience, companies can convert silicon advantages into system level value. The synthesis presented here offers a strategic foundation for executives and technical leaders seeking to navigate the complex trade offs inherent in designing, manufacturing and commercializing next generation deep learning hardware.
Please Note: PDF & Excel + Online Access - 1 Year
A strategic introduction to deep learning chipsets highlighting core technological enablers, commercialization dynamics, and integration pathways for hardware and software synergy
This executive summary introduces the contemporary deep learning chipset landscape by focusing on technological imperatives, commercial dynamics, and the interaction between hardware and software ecosystems. Advancements in algorithmic complexity and rising inference and training workloads have elevated the role of specialized accelerators, prompting a closer alignment between semiconductor design, system integration, and application requirements. As a result, development roadmaps increasingly prioritize power efficiency per inference, interconnect performance, and software stack maturity to ensure solution-level differentiation.
The narrative that follows situates chipset evolution within broader industry transitions, where heterogeneity and modularity are critical. Developers and systems architects are prioritizing programmability alongside raw throughput, while OEMs and hyperscalers balance capital intensity against deployment flexibility. In this environment, the ability to translate model architecture innovations into sustained hardware advantage depends on robust IP portfolios, efficient production relationships, and a well-defined route to market. By understanding these drivers and constraints, stakeholders can make informed choices about partnership structures, product roadmaps, and integration strategies that align with application-specific demands.
Converging forces reconfiguring compute architectures and data pipelines to accelerate training and inference workloads across heterogeneous accelerators and distributed deployments
The landscape for deep learning chipsets has experienced transformative shifts that reconfigure design priorities, go-to-market approaches, and deployment topologies. Heterogeneous compute stacks have moved from experimental deployments to mainstream adoption, with accelerators designed for specialized matrix operations coexisting alongside general purpose processors. This transition is accompanied by a stronger emphasis on software abstraction layers, enabling models to be portable across ASICs, GPUs, FPGAs and CPUs while preserving performance characteristics.
At the same time, the locus of compute is diversifying: cloud-native training clusters remain critical for large scale model development, but edge and on premise inference deployments are becoming decisive for latency sensitive and privacy constrained applications. Supply chain dynamics have also shifted, pushing firms to localize critical capabilities or identify second source manufacturers to mitigate geopolitical risk. These combined forces are driving a more modular approach to chipset design, with architectures and packaging optimized for thermal envelopes, power budgets, and system-level interoperability rather than single metric supremacy.
Assessment of tariff policy shifts and trade measures affecting supply chains, procurement strategies, and cross border manufacturing for deep learning semiconductor components
Trade measures instituted by the United States and allied policy adjustments through recent years have created persistent implications for semiconductor supply chains, procurement strategies, and manufacturing decisions relevant to deep learning chipsets. Tariff schedules and related export controls have increased the cost and complexity of cross border component flows, influencing decisions about where to locate high value fabrication, assembly, and testing activities. Firms with vertically integrated supply chains or diversified manufacturing footprints are better positioned to absorb such policy driven shifts.
Beyond immediate cost considerations, these trade actions have catalyzed strategic reorientation across ecosystem participants. Original device designers and foundry partners are reassessing long lead time orders, contractual flexibility, and inventory policies to maintain continuity of supply. In addition, equipment vendors and downstream integrators are adapting sourcing strategies to favor regional suppliers and to internalize certain capabilities that were previously outsourced. The cumulative effect has been a reallocation of investment toward supply resilience, alternative sourcing arrangements, and enhanced visibility across multi tier supply networks, all of which influence product roadmaps and time to integration.
In depth segmentation insights revealing device classes, deployment modalities, end user behaviors, and application specific requirements shaping architecture and value chains
Segmentation analysis reveals differentiated drivers and adoption patterns across device types, deployment modes, end users, and application clusters that shape chipset requirements and value propositions. Device distinctions matter: ASICs are being optimized for cost efficient, high density inference; GPUs continue to dominate large scale training and flexible model experimentation; FPGAs retain relevance where reprogrammability and low latency customization are paramount; and CPUs remain essential for control plane tasks and mixed workload orchestration. Each device class imposes distinct constraints on power, latency, and software support, creating clear trade offs for system architects.
Deployment modalities further refine design priorities. Cloud deployments prioritize scalability, multi tenancy and thermal efficient data center packaging, whereas edge implementations emphasize robust thermal management, minimal power draw and local inferencing reliability. On premise deployments often combine the two priorities, requiring enterprise grade security controls and simplified lifecycle management. End user segmentation also delineates expectations: consumer applications demand compact form factors, cost sensitivity and seamless integration into everyday devices, while enterprise customers emphasize uptime guarantees, manageability and compliance with regulatory requirements.
Application verticals place additional, granular constraints on chipset selection and complementary software. Autonomous vehicle systems require fault tolerant compute with specialized ADAS processing and pathways toward fully autonomous stacks, demanding stringent functional safety and deterministic latency. Consumer electronics prioritize energy efficiency and seamless user experiences across smart home devices, smartphones and wearables. Data center priorities split between cloud scale orchestration and on premise enterprise clusters, while healthcare applications emphasize diagnostic accuracy, medical imaging throughput and patient monitoring reliability. Robotics encompasses industrial control precision and service robotics adaptability. Together, this segmentation matrix informs product roadmaps, supplier selection, and co engineering efforts that drive system level differentiation.
Regional dynamics and competitive advantages across the Americas, Europe Middle East and Africa, and Asia Pacific influencing talent, supply and adoption pathways
Regional conditions create distinct competitive advantages, infrastructure profiles, and regulatory contexts that materially influence chipset strategies and adoption pathways. The Americas benefit from a dense concentration of hyperscale cloud providers, leading edge design houses and a mature venture ecosystem that accelerates commercialization cycles and fosters deep talent pools in chip design and systems integration. This environment encourages rapid prototyping, close collaboration between software teams and hardware architects, and early enterprise adoption across cloud centric use cases.
Europe, Middle East & Africa present a heterogeneous set of policy and industrial dynamics, with strong emphasis on data protection, industrial automation, and the integration of semiconductor strategy into broader industrial policy. Regional incentives for localized manufacturing, coupled with regulatory expectations around data sovereignty, drive unique deployment models that favor on premise and hybrid solutions for sensitive applications, including healthcare and critical infrastructure.
Asia Pacific combines vast manufacturing capacity, integrated supply chains, and rapidly growing end user markets that span consumer electronics to industrial automation. The region’s strengths in high volume production, component ecosystem depth, and cross border logistics create favorable conditions for scaling new chipset designs into mass market devices while also providing competitive specialization in packaging, testing and assembly services. Across all regions, talent availability, capital access, and policy signals shape where and how firms prioritize investments along the product life cycle.
Corporate competitive intelligence on chipset vendors, ecosystem partners, and new entrants emphasizing differentiation, partnerships, IP strategies and capital allocation priorities
Competitive dynamics among chipset providers and ecosystem partners are accelerating, with established semiconductor vendors, cloud hyperscalers and specialized AI accelerator startups each pursuing distinct routes to differentiation. Legacy GPU vendors continue to expand software stacks and developer ecosystems to protect broad applicability across training and inference, while custom ASIC efforts focus on vertical specialization and tighter integration with leading model architectures. FPGA based solutions are capitalizing on reprogrammability to target niche latency sensitive and privacy constrained applications where hardware level customization yields measurable benefits.
Partnerships and IP strategies are central to competing effectively in this environment. Strategic alliances between chip designers, foundries, and systems integrators reduce time to integration and provide access to critical manufacturing capacity. New entrants emphasize unique microarchitectures, large scale wafer access agreements, or novel packaging approaches to create defendable positions. Investment in developer tooling, model compilers, and runtime optimization is increasingly as important as raw silicon capability, and firms that cultivate a robust software ecosystem gain sustained commercial advantages. Capital allocation priorities reflect a balance between R&D intensity, go to market scaling, and long term investments in supply chain resiliency.
Actionable strategic recommendations for industry leaders to optimize R D investments, supply resilience, go to market models, and ecosystem orchestration for sustained advantage
Industry leaders should organize strategy around resilient supply chains, prioritized software investments, selective partnerships, and outcome oriented product roadmaps. First, establish multi path sourcing strategies and deepen collaboration with assembly and test partners to reduce single point dependencies. This enhances continuity and enables tactical responsiveness to trade policy adjustments and logistical shocks. Second, invest deliberately in software tooling and compiler optimization that enable model portability across ASIC, GPU, FPGA and CPU targets, thereby reducing integration friction and broadening addressable deployments.
Third, pursue targeted partnerships with hyperscale customers, OEMs and application specialists to co develop solutions that match unique operational constraints and accelerate adoption. Fourth, align product roadmaps with concrete application level metrics-such as latency targets, energy per inference and safety certifications-so that engineering efforts are directly tied to end user outcomes. Finally, embed regulatory and policy monitoring into strategic planning, ensuring that procurement cycles, localization choices and IP protection strategies adapt proactively to evolving trade measures and data governance requirements. These steps position organizations to capture strategic opportunities while maintaining flexibility in an uncertain geopolitical environment.
Transparent research methodology describing primary and secondary approaches, expert interviews, data triangulation, and quality assurance practices underpinning the analysis
The research approach underpinning this analysis relies on a combination of primary expert engagement, targeted technical review, and structured secondary research to ensure robust, reproducible insights. Primary inputs included structured interviews with chipset architects, systems integrators, hyperscaler infrastructure leads, and end user IT decision makers, supplemented by technical briefings with foundry and packaging specialists. These engagements provided granular visibility into design priorities, integration challenges, and procurement considerations across deployment contexts.
Secondary research involved synthesis of publicly available technical papers, regulatory filings, vendor product documentation and open industry standards to validate technical claims and situate strategic trends. Data triangulation methods reconciled divergent perspectives by cross checking supplier roadmaps, end user requirements, and observed deployment patterns. Quality assurance included peer review of analytical assumptions, reproducibility checks on technical performance interpretations, and sensitivity analysis around policy scenarios to ensure conclusions remain robust under plausible conditions. This blended methodology supports a balanced, evidence driven view of chipset dynamics and implications for stakeholders.
Concluding synthesis distilling core findings, strategic implications for stakeholders, and the pragmatic next steps for technology, policy and commercial engagement
In conclusion, the evolution of deep learning chipsets is defined by a convergence of architectural heterogeneity, supply chain reconfiguration, and application specific demands that together shape commercial viability and technical differentiation. The most successful players will couple focused silicon innovation with mature software ecosystems, resilient manufacturing relationships, and targeted partnerships that reduce integration friction for end customers. Strategic clarity around deployment modalities-cloud, edge and on premise-enables organizations to tailor product features to measurable customer outcomes and regulatory constraints.
Policy developments and regional dynamics are now integral to technology strategy, requiring that firms embed geopolitical risk assessment into product planning and supplier selection. By aligning R&D priorities with application led requirements and by investing in developer experience, companies can convert silicon advantages into system level value. The synthesis presented here offers a strategic foundation for executives and technical leaders seeking to navigate the complex trade offs inherent in designing, manufacturing and commercializing next generation deep learning hardware.
Please Note: PDF & Excel + Online Access - 1 Year
Table of Contents
194 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Integration of chiplet-based 2.5D packaging for scalable large language model accelerators
- 5.2. Development of photonic interconnect channels in deep learning processors to minimize data transfer latency
- 5.3. Specialized integer and mixed-precision matrix engines optimized for transformer-based inference workloads
- 5.4. Emergence of RISC-V open accelerator ecosystems enabling custom AI instruction sets and extensibility
- 5.5. Advanced dynamic voltage and frequency management for workload-aware energy-efficient AI training
- 5.6. Collaborative design partnerships between hyperscalers and silicon vendors for co-optimized AI stacks
- 5.7. On-device micro AI chipsets delivering sub-millisecond real-time inference in battery-powered edge sensors
- 5.8. Neuromorphic spiking neural network processors accelerating sparse event-driven machine intelligence
- 5.9. Integration of secure cryptographic accelerators with neural network inference engines to protect model IP
- 5.10. Adoption of 3D-stacked high-bandwidth memory in AI chiplets to meet rising transformer parameter demands
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Deep Learning Chipset Market, by Device Type
- 8.1. ASIC
- 8.2. CPU
- 8.3. FPGA
- 8.4. GPU
- 9. Deep Learning Chipset Market, by Deployment Mode
- 9.1. Cloud
- 9.2. Edge
- 9.3. On Premise
- 10. Deep Learning Chipset Market, by End User
- 10.1. Consumer
- 10.2. Enterprise
- 11. Deep Learning Chipset Market, by Application
- 11.1. Autonomous Vehicles
- 11.1.1. ADAS
- 11.1.2. Fully Autonomous
- 11.2. Consumer Electronics
- 11.2.1. Smart Home Devices
- 11.2.2. Smartphones
- 11.2.3. Wearables
- 11.3. Data Center
- 11.3.1. Cloud
- 11.3.2. On Premise
- 11.4. Healthcare
- 11.4.1. Diagnostic Systems
- 11.4.2. Medical Imaging
- 11.4.3. Patient Monitoring
- 11.5. Robotics
- 11.5.1. Industrial Robotics
- 11.5.2. Service Robotics
- 12. Deep Learning Chipset Market, by Region
- 12.1. Americas
- 12.1.1. North America
- 12.1.2. Latin America
- 12.2. Europe, Middle East & Africa
- 12.2.1. Europe
- 12.2.2. Middle East
- 12.2.3. Africa
- 12.3. Asia-Pacific
- 13. Deep Learning Chipset Market, by Group
- 13.1. ASEAN
- 13.2. GCC
- 13.3. European Union
- 13.4. BRICS
- 13.5. G7
- 13.6. NATO
- 14. Deep Learning Chipset Market, by Country
- 14.1. United States
- 14.2. Canada
- 14.3. Mexico
- 14.4. Brazil
- 14.5. United Kingdom
- 14.6. Germany
- 14.7. France
- 14.8. Russia
- 14.9. Italy
- 14.10. Spain
- 14.11. China
- 14.12. India
- 14.13. Japan
- 14.14. Australia
- 14.15. South Korea
- 15. Competitive Landscape
- 15.1. Market Share Analysis, 2024
- 15.2. FPNV Positioning Matrix, 2024
- 15.3. Competitive Analysis
- 15.3.1. Advanced Micro Devices, Inc.
- 15.3.2. Apple Inc.
- 15.3.3. ARM Limited
- 15.3.4. BrainChip Holdings Ltd.
- 15.3.5. Cambricon Technologies Corporation Limited
- 15.3.6. Cerebras Systems, Inc.
- 15.3.7. CEVA, Inc.
- 15.3.8. Google LLC
- 15.3.9. Graphcore Limited
- 15.3.10. Groq, Inc.
- 15.3.11. Huawei Technologies Co., Ltd.
- 15.3.12. Intel Corporation
- 15.3.13. International Business Machines Corporation
- 15.3.14. KnuEdge, Inc.
- 15.3.15. Mythic, Inc.
- 15.3.16. NVIDIA Corporation
- 15.3.17. Qualcomm Technologies, Inc.
- 15.3.18. Samsung Electronics Co., Ltd.
- 15.3.19. TeraDeep, Inc.
- 15.3.20. Wave Computing, Inc.
- 15.3.21. Xilinx, Inc.
Pricing
Currency Rates
Questions or Comments?
Our team has the ability to search within reports to verify it suits your needs. We can also help maximize your budget by finding sections of reports you can purchase.
