Problem First: How to Make Hardware and Models Tailored for AI Agents

Today, let’s delve into problem-oriented approaches in technology. We will focus on the differences between computer engineering and computer science, general-purpose CPUs versus tailored hardware, hybrid solutions like FPGAs, and how these ideas can revolutionize AI hardware and models. Let’s extract key insights from these topics and explore how a “problem-first” mindset can transform AI agents.

Problem define Algorithm

Quite often, we have an opposite direction we see a problem through a prism and optics of patterns, algorithms, and general-purpose tools. Sometimes we need to step back and find our way

Computer Engineering vs. Computer Science

In academia, computer engineering and computer science often diverge after foundational studies. Computer engineers focus on designing hardware and optimizing it for specific constraints, such as size, power consumption, and heat dissipation. On the other hand, computer scientists primarily deal with abstract concepts like algorithms, patterns, and software systems constrained by general-purpose CPUs.

Key Differences:

• Computer Science: Leverages abstract algorithms and known patterns, emphasizing adaptability and versatility in solving problems.
• Computer Engineering: Designs custom hardware to meet specific needs, focusing on efficiency and tailored solutions. The approach is often driven by the problem itself, not existing tools or algorithms.

This distinction lays the groundwork for understanding why AI hardware requires a unique approach — one that balances computational flexibility and efficiency.

General-Purpose CPU vs. System-on-Chip (SoC) and Tailored Solutions

General-purpose CPUs dominate traditional computing due to their flexibility. However, this flexibility comes at a cost: inefficiency for specialized tasks. In contrast, systems-on-chip (SoCs) integrate specific components tailored for particular tasks, offering better performance, lower power consumption, and reduced heat output.

• General-Purpose CPUs: Adaptable but constrained by inefficiencies in specialized operations like AI inference.
• SoCs: Tailored solutions that excel in specific applications, such as mobile devices or embedded systems. Modern SoCs often integrate GPUs or neural processing units (NPUs) for AI tasks.

The success of SoCs highlights the benefits of designing hardware with a “problem-first” approach, where constraints define the architecture rather than retrofitting general-purpose designs.

Hybrid FPGA Solutions and How Mindsets Combine

Field-Programmable Gate Arrays (FPGAs) bridge the gap between fixed hardware and software flexibility. They allow engineers to program hardware configurations dynamically, combining software adaptability with custom hardware's efficiency.

• Advantages of FPGAs:
• Programmable to suit specific workloads.
• Energy-efficient compared to general-purpose CPUs or GPUs.
• Enable rapid prototyping of AI accelerators.

This hybrid mindset integrates computer science and computer engineering strengths, fostering a collaborative environment where tools and problems co-evolve.

Transforming Hardware for AI: A New Paradigm

AI has unique demands: high computational throughput, low latency, and energy efficiency. These requirements challenge traditional hardware paradigms. A “problem-first” perspective in AI hardware development focuses on:

• Tailored Architectures: Designing hardware specifically for AI tasks like neural network inference or training.
• Optimized Components: Incorporating specialized processors such as tensor processing units (TPUs) or neural engines.
• Energy Efficiency: Minimizing power consumption to make AI systems viable in edge environments.

So Special Systems on Chip gets back to the game. Even GPU is still a example general processing unit designed more for image processing and video games. We could be more AI-focused. We also could build Vector and tensor-oriented accelerators and computation units or go even more problem-focused and create transformer architecture in hardware.

Systems-on-Chip (SoCs) for AI Inference

AI inference — the execution phase of AI models — demands efficient, low-power solutions. SoCs designed for AI integrate:

• NPUs: Accelerators optimized for neural network operations.
• Efficient Memory Hierarchies: Minimizing latency and maximizing throughput for AI workloads.
• Compact Designs: Allowing deployment in constrained environments, such as IoT devices or autonomous drones.

By tailoring SoCs for AI inference, engineers achieve better performance and energy efficiency compared to repurposing general-purpose hardware.

General-Purpose LLMs vs. Tailored Models

Large language models (LLMs) like GPT are versatile but resource-intensive. A “problem-first” approach suggests an alternative: developing smaller, specialized models (SLMs) for specific tasks. Additionally, tailored machine learning (ML) models can address specific challenges in AI memory systems, providing a practical example of problem-oriented thinking.

General-Purpose LLMs:

• Versatile but require significant computational resources.
• Prone to inefficiencies and “hallucinations.”
• Tailored Models (SLMs):
• Focused on specific tasks (e.g., episodic memory classification or graph construction) but quite often are distilled LLM and still have more generalistic core
• More efficient and accurate for their intended purposes.

Specialized ML Models in AI Memory:

We could get back to the game old classical ML models that could be trained for a single purpose and be more predictable. For example we could decompose challenge of AI agentic memory model as a set of models

1. Temporal Context Extraction Models: Designed to identify time-based relationships within data, enabling AI agents to understand and process sequences of events effectively.
2. Event Context Extraction Models: Focused on discerning meaningful events from unstructured data, facilitating better decision-making by agents.
3. Episodic and Semantic Memory Classifiers: Specialized models that differentiate between memory types, ensuring accurate retrieval and storage of contextual information.
4. Semantic Graph Construction Models: These models transform unstructured data into structured graphs, enabling better organization and ontology learning.

By combining these specialized models, AI agents can achieve better efficiency and accuracy in tasks like memory management, reducing the overhead associated with general-purpose LLMs.

AI Agents and the Orchestration of Specialized Models

AI agents are evolving to leverage multiple specialized models, orchestrating them to achieve complex goals. This requires:

• Orchestration Layers: Frameworks that coordinate various models to solve distinct aspects of a problem.
• Task-Specific Agents: Specialized agents for memory classification, context extraction, or graph construction.
• Collaborative Systems: Combining tailored models into a cohesive, efficient system.

For example, an agent tasked with processing unstructured text might:

• Use a temporal context model to extract sequences of events.
• Apply an event context model to identify significant occurrences.
• Leverage a semantic memory classifier to store relevant information.
• Utilize a graph construction model to create structured outputs for downstream tasks.

This agentic framework represents the next frontier in AI, blending modularity with specialization.

Problem-First Thinking in Hardware, AI Models, and Agents

A “problem-first” mindset prioritizes understanding and defining the problem before designing solutions. This approach is critical in:

• Hardware: Tailoring systems for specific AI workloads, such as inference or training.
• AI Models: Developing specialized models to address distinct tasks precisely and efficiently.
• AI Agents: Orchestrating multiple specialized components to create intelligent systems.

By focusing on the problem first, we can design more effective, efficient, and purpose-driven solutions for the challenges of modern AI.

On the opposite side of this approach, full custom and tailored solutions are expensive in production and heavenly constrained in evolution, adaptability, and changes.

We need a practical and cost-effective solution combining a Hybrid FPGA approach where we could experiment and adopt a system after synthesizing the final solution.