Technology Platform Selection Guide for High-Complexity Products

Even the most experienced hardware engineers have moments of doubt when staring at a project schedule and countless datasheets, wondering, "How do I know I'm making the right choice?"

While selecting technology based solely on specifications seems systematic, how can you truly ensure the platform you build your solution on will deliver long-term success for your finished product and prove its value to stakeholders?

The early decisions you make on underlying technology platforms and system architecture can determine whether you achieve your objectives. Certain goals may be clear from the beginning, such as functional performance metrics and launch schedule, while others, such as user-requested features and the long-term technical maintenance burden, may be unknown at project kickoff, but no less impactful. So why not approach these critical decisions with a proven framework that transforms uncertainty into confidence and mitigates risk, even for some of the unknowns?

The challenge isn't just technical, it's strategic.

Engineering teams today face an overwhelming array of processing architectures, form factors, and software stack design decisions, all while navigating the core trade-offs between system performance, budget, and development speed.

Without the right criteria for choosing a technology stack to build their solution on, many fail to attain their market objectives. For example, many face the common pitfall of prematurely optimizing unit costs, which can significantly delay launch schedule, market uptake, and time-to-profit. Nothing is more expensive than failing to get to market at all.

Below are a few examples of applications with high-complexity requirements where platform selection is non-trivial.

• Infrastructure monitoring systems (extreme environments, long lifecycle, remote deployment, total cost of ownership)

• High-speed automation processes (microsecond-level determinism, real-time performance, industrial networks)

• Healthcare edge devices (compliance requirements, security architecture)

• Equipment protection systems (fail-safe operation, environmental hardening)

• Industrial IoT AI inference systems (edge processing, model lifecycle management)

This comprehensive white paper series aims to provide engineering teams with a structured methodology for evaluating product development platforms across a wide range of application spaces. We'll guide you beyond surface-level specifications to the considerations and factors that determine success from initial research through long-term product sustainment.

Our selection framework for high complexity, medium volume product deployments addresses eight critical evaluation dimensions that separate successful deployments from costly mistakes:

1. Signal Integration & I/O Mix

2. Processing & Compute

3. Software Toolchain

4. Deployment Environment

5. Cost Models

6. Security Architecture

7. AI Integration

   
   

Signal Integration & I/O Mix

Why it’s important:

Limited I/O options can become expensive problems quickly. Products today are differentiated through their ability to integrate diverse signals, sensors, actuators, and protocols seamlessly. A platform lacking in native I/O diversity can force costly workarounds onto your team: additional hardware, increased complexity, and unforeseen sustainment costs can compound over a product’s lifecycle. Comprehensive I/O integration is more than convenient; it can prevent development bottlenecks and keep overall system costs down.

We recommend evaluating I/O capabilities using the following criteria:

1. High-quality, calibrated measurement and stimulus: The accuracy and precision of high quality, calibrated I/O ensure that the analog and digital interfaces in your system are meeting your application requirements reliably. Measurement uncertainty can compound through an entire system; uncalibrated I/O can wreak havoc upon the most sophisticated algorithms and control strategies.

   
   

2. Expansion and scalability: A system’s ability to accommodate additional I/O channels and signal types, without requiring architectural changes or separate hardware platforms, helps mitigate sustainment risks and facilitates releases throughout CI/CD processes. System requirements nearly always expand over time through customer requests and continuous improvement efforts, making flexible, modular I/O necessary for maintaining development momentum and avoiding costly redesigns.

   
   

3. Future-proofed protocol support: A system’s native ability to interface with industrial networks, and emerging IoT standards without requiring external gateways or protocol converters can facilitate seamless integration with existing infrastructure and help futureproof a system against evolving communication standards and requirements.

   
   

Implementation Considerations & Guidance

• Audit your complete signal ecosystem upfront: catalog every sensor, actuator, and communication protocol you need today and anticipate for future releases to avoid architectural surprises

• Prioritize factory calibrated, modular I/O platforms: measurement errors compound through your system, and requirements always expand; choose platforms that maintain accuracy while adding channels without redesigns

• Select platforms with native protocol support: map your industrial networks and IoT requirements early; native communication protocol support eliminates costly gateways and future integration headaches

Processing & Compute

Why it's important:

High-performance embedded systems demand precise timing you can rely on. Advanced control algorithms, safety-critical control loops and industry-specific compliance can require real time performance. Desktop operating systems and general purposes microcontroller units (MCUs) cannot guarantee critical response times for protection systems and high-speed automation.

We recommend evaluating real-time computation capabilities using the following criteria:

1. Compute architecture: Safety-critical and equipment monitoring applications require an underlying system design that enables deterministic, predictable execution of time-critical tasks without interference from non-critical processes. Without a hardware and software solution that can ensure minimal timing jitter in the system, it’s possible that equipment damage or other hazardous conditions could arise.

   
   

2. Performance & loop rates: A system’s ability to execute control algorithms and data processing tasks at required frequencies is critical to system integrity. If loop rates were to fall below the tolerances of the controlled system, control system stability and performance can rapidly degrade.

   
   

3. Memory management: The response time of a real-time system is dependent on the rate at which critical tasks can access the data they need. Memory access latencies and cache misses can introduce timing jitter issues that would violate the system's real-time constraints and compromise overall system safety.

Implementation Considerations & Guidance

• Define your critical timing requirements precisely: identify which control loops, safety functions, and monitoring tasks require microsecond determinism versus those that can tolerate standard OS scheduling

• Choose a compute architecture based on timing criticality: Refer to Table 1 for a comparison of GPUs, microcontrollers, CPUs, and FPGAs

• Choose a dedicated real-time compute architecture: safety-critical applications need hardware-software solutions that guarantee deterministic execution without interference from non-critical processes

• Validate performance under worst-case conditions: test your required loop rates and response times with full system loading, not just isolated benchmark conditions

• Design memory architectures for predictable access: minimize cache misses and memory latencies for time-critical tasks; deterministic memory access is essential for maintaining real-time constraints

Software Toolchain

Why it's important:

Software can be your team’s greatest differentiator, or it can completely derail your development timelines. High-complexity systems require customization across the entire software stack, but fragmented toolchains and siloed development environments can result in debugging, integration, and iteration becoming costly ordeals. The software toolchain chosen can determine the balance between the flexibility to customize and the overhead to integrate. These tradeoffs must be considered during toolchain selection as architectural decisions quickly propagate throughout development and could make it impossible to pivot mid-project.

We recommend evaluating software design and toolchain choice using the following criteria:

1. Development Speed and Flexibility: Toolchain selection and software stack architecture determine how flexible your product will be to adapt to evolving and future requirements. Familiarity with software can support a rapid implementation of IP and the learning curve of an un familiar software stack can be mitigated through extensive documentation and an intuitive user experience out-of-the-box.

   
   

2. Open source vs. custom IP: Any software developer must balance the use of proven, open-source code to accelerate their overall development time and the generation of new, proprietary intellectual property (IP) that will most greatly impact their product’s differentiation.

   
   

3. Abstraction layers: Abstraction layers provide standardized interfaces that isolate application logic from underlying hardware and system dependencies, which is vital for maintaining code portability, enabling future hardware upgrades, and reducing the risk of vendor lock-in across long product lifecycles.

   
   

4. Build and deployment tools: These tools are the automated systems for compiling, testing, packaging and distributing software across development, testing, and production environments. They are essential for maintaining code quality, mitigating deployment errors and enabling rapid iteration cycles that can keep pace with customer feedback.

Implementation Considerations & Guidance

• Assess toolchain selection and software stack architecture early on: select development tools and a software stack that minimize integration challenges cross engineering teams

  
  

• Balance proven libraries with competitive differentiation: leverage native libraries and hardware abstraction layers to implement background processes and common functionality. Doing so enables you to free up your development resources to focus on implementing your unique IP. This approach accelerates your product’s time-to-market while creating a more maintainable codebase that sustains your competitive advantage throughout the product lifecycle

  
  

• Implement automated build and deployment pipelines early: establish CI/CD workflows from project start to maintain code quality, reduce deployment errors, and enable rapid iteration with customer feedback

Deployment Environment

Why it's important:

The deployment environment of your application is a design constraint that should inform hardware selection from day one. Harsh conditions require component derating, conformal coating, specialized enclosures, thermal management, rigorous field testing, and hazardous area classifications. It’s critical to consider the environmental realities of your application to avoid the need for costly redesigns that could compromise performance and delay your product’s time to market.

We recommend evaluating deployment environment capabilities using the following criteria:

1. Ambient conditions: A system’s ability to operate reliably across the full range of environmental stressors are subject to the environment the system is deployed into. These environmental factors, like temperature, humidity, vibration, and electromagnetic interference are typically the leading causes of field failures. Systems that cannot withstand their deployment conditions will require frequent maintenance interventions, which will ultimately negate any operational benefits gained through the implementation of the system.

   
   

2. Thermal management: The ability of a system to dissipate heat generated by its processing and I/O components will determine its ability to maintain safe operating temperatures across varying ambient conditions and computational loads. Thermal stress accelerates component aging and can cause intermittent failures that could be costly to diagnose and repair when deployed in remote locations. Operating ranges and environmental derating are important to mitigate system stress and safety issues, prevent premature failure and extend asset lifespan.

   
   

3. Physical and networked connectivity: All connection points, including I/O terminals, communication ports, and network interfaces must operate reliably despite environmental factors. The robustness of these connections is vital to system operation and are a very common failure point for systems deployed in the field.

   
   

4. Hazardous area classifications: Systems deployed into environments with explosive atmospheres or flammable materials require hazardous area certifications (e.g. ATEX, IECEx, NEC Class/Division ratings). This regulatory requirement fundamentally impacts hardware selection, enclosure design and system architecture. Obtaining certifications for custom hardware can add substantial time and cost to product development projects, making platforms with existing approvals valuable for accelerating market entry.

Implementation Considerations & Guidance

• Characterize your full environmental envelope: measure actual temperature ranges, vibration levels, EMI sources, and contamination in your deployment location, as laboratory specs rarely match field conditions. One common way to manage this is to build sensors into products that can self-calibrate in the field. For example, a temperature readback sensor signals the device to activate a fan for cooling purposes once the operating temperature threshold has been surpassed.

  
  

• Design thermal management for worst-case scenarios: ensure your system can dissipate heat at maximum computational load combined with highest ambient temperatures; thermal stress is a primary cause of field failures

  
  

• Harden all connection points from day one: specify industrial-grade I/O terminals, sealed communication ports, and robust network interfaces; connection failures are among the most common and costly field issues

  
  

• Plan for maintenance accessibility: consider how environmental factors affect your ability to service, diagnose, and replace components during the system's operational lifetime

Cost Models

Why it's important:

In low-to-medium volume production, optimizing unit costs alone and ignoring development speed, flexibility and time-to-market can result in delayed project timelines or worse, missed market windows. Custom hardware design typically results in extensive bring-up phases and a costly ongoing support burden, erasing any marginal unit cost savings and limiting engineering bandwidth to focus on high-ROI tasks.

We recommend evaluating cost models using the following criteria:

1. Off-the-shelf vs. custom: Deciding whether to develop custom hardware and software solutions, down to circuit board and low-level software design, internally or leveraging existing commercial platforms with proven capabilities can impact development costs exponentially. Custom development often appears cost-effective at small scales but introduces the risks of extended development timelines and untenable maintenance and sustainment burdens.

   
   

2. Hardware unit cost: When a product team focuses solely on the per-device expense for processing, I/O, and connectivity components at projected production volumes they inherently introduce risk into overall project costs and profitability. Unit costs directly impact product margins and competitiveness in the market, but it must be evaluated alongside development and integration costs to understand true cost of getting a product to market and sustaining it long-term.

   
   

3. Non-recurring engineering (NRE) and development costs: All the upfront investments in hardware design, software development, testing and certification required to bring a product to market must be amortized across the total production volume. Underestimating development complexity can turn the most seemingly profitable projects into massive financial headaches.

Implementation Considerations & Guidance

• Calculate true development ROI across your volume projections: custom solutions may seem cheaper per unit but factor in extended development timelines, testing costs, and ongoing maintenance burdens against proven commercial platforms

  
  

• Model total cost of ownership (TCO): Explore costs beyond hardware unit cost. TCO encompasses all expenses throughout a product’s lifecycle, including non-recurring engineering (NRE) costs, field service expenses, software updates, certification requirements, and end-of-life management. Project the lifetime expenses of a technology stack from initial development to sustainment over the product’s operational life to determine the total cost of ownership. 

  
  

• Establish realistic volume assumptions early: accurately project your deployment scale to properly amortize development investments; overestimating volumes can make custom development appear falsely attractive

  
  

• Plan for hidden integration and sustainment costs: budget for ongoing technical support, security updates, hardware obsolescence management, and field service requirements that often exceed initial hardware expenses

Security Architecture

Why it's important:

Every connected edge device is a potential doorway into critical systems. As edge computing spreads across industrial and infrastructural environments, these devices are increasingly becoming prime targets for cybercriminals hoping to explore vulnerable entry points to operational networks, sensitive data, and control systems. Without robust security architecture built into your platform from the start, functionality could be deployed alongside vulnerabilities and attack vectors at scale.

We recommend evaluating security and compliance capabilities using the following criteria:

1. System security: The hardware and software security features that influence the device integrity, data confidentiality, and operational availability of your product against external threats. Edge devices are often the most vulnerable to remote attacks and physical tampering due to their deployment in isolated locations that can be difficult to surveil. A compromised edge device can expose sensitive data or compromise the network infrastructure it is a part of.

   
   

2. Security package integration: The ability of a platform to natively incorporate industry-standard security frameworks, encryption libraries, and authentication protocols directly impacts development efforts and timelines. Security implementation is complex and prone to errors. Organizations without deep cybersecurity implementation expertise need platforms with proven security capabilities out-of-the-box to protect their assets and ensure network security.

   
   

3. Compliance: The platform’s ability to meet regulatory requirements and industry standards for cybersecurity, data protection, and operational security across markets and applications are vital to widespread product adoption and customer confidence. Non-compliance can result in regulatory fines, customer rejection, and liability exposure.

   
   

Implementation Considerations & Guidance:

• Assess your full attack surface from device to cloud: map all connection points, data flows, and access vectors; edge devices in remote locations are particularly vulnerable to both cyber-attacks and physical tampering

  
  

• Choose platforms with proven security frameworks built-in: leverage native encryption, authentication, and security protocols rather than developing custom solutions; security implementation is complex and error-prone

  
  

• Identify compliance requirements early in design: determine which regulatory standards (NIST, IEC 62443, etc.) apply to your markets and ensure your platform can meet these requirements without extensive customization

  
  

• Plan for security lifecycle management: establish processes for security updates, certificate management, and vulnerability response across your deployed device fleet's operational lifetime

  
  

Table 2. Security considerations are industry-specific and highly dependent on the type of application being deployed. An exhaustive assessment of attack vectors and security requirements are essential to mitigate system vulnerabilities. Note: security is always a growing and evolving consideration

AI Integration

Why it's important:

Reliance on the cloud for critical systems introduces a failure mode to any application; mission critical functionality cannot depend on the cloud for processing and decision-making. AI inference at the edge enables real-time applications in settings where connectivity is intermittent or impossible. Local AI processing with millisecond-level responsiveness is essential for data breakthroughs at the edge.

We recommend evaluating AI and machine learning capabilities using the following criteria:

1. Inference at the edge: A platform’s ability to execute trained AI models locally on an edge device enables your most dynamic IP to perform mission critical tasks reliably. Cloud-dependent AI systems introduce latency and reliability risks into real-time control and safety applications, whereas local inference at-the-edge enables your system to respond immediately to changing conditions without sacrificing real-time performance.

   
   

2. Model training: The ability to update and refine AI models using local data are foundational aspects of an edge device’s performance. AI models must adapt to changing operational conditions, equipment variations and evolving requirements that your team cannot anticipate during development. A suitable edge device must be capable of supporting on-device training or seamless integration with model training workflows.

   
   

3. Data flow & validation: Any edge device running AI inference must reliably and efficiently manage the movement, preprocessing, and quality assurance of the data sets used. AI model performance is dependent on data quality and consistency and therefore must handle data validation, anomaly detection, and selective data transmission without overwhelming network resources or compromising sensitive information.

Implementation Considerations & Guidance:

• Validate inference performance under real operational conditions: test your AI models on actual edge hardware with realistic data loads, environmental conditions, and concurrent system tasks to ensure reliable real-time performance

  
  

• Design for model lifecycle management: establish workflows for updating, retraining, and validating AI models using field data while maintaining system safety and performance during updates

  
  

• Implement robust data preprocessing and validation pipelines: ensure your edge platform can handle data quality assurance, anomaly detection, and selective transmission without overwhelming network resources or exposing sensitive information

  
  

• Plan compute resources for AI workload scaling: size your processing capabilities for peak inference demands while considering future model complexity growth and additional AI applications over the product lifecycle