Hardware Design
Selode.AI explores inference engine design with a specific focus on the hardware characteristics that shape real-world AI performance. Our work considers throughput, latency, memory behaviour, deployment constraints, power efficiency, and performance-per-watt as fundamental design variables across enterprise and edge environments.
In parallel, Selode.AI has been exploring the commercial utility of FPGA in conjunction with AMD, with particular interest in adaptable acceleration pathways for specialised inference, hardware-aware optimisation, and deployment scenarios where configurability and efficiency matter.
Inference Engine Design
This work is oriented toward practical AI systems: aligning model execution with the physical realities of compute architecture, memory access, thermal envelope, and deployment environment. The objective is not only performance, but commercially usable hardware pathways for efficient and scalable inference.
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Inference Pathway Design Design exploration focused on execution behaviour, workload alignment, and practical architectures.
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Hardware Characteristic Focus Attention to latency, throughput, memory access patterns, and performance-per-watt.
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Commercial FPGA Utility Exploration of FPGA as a configurable pathway for specialised inference acceleration.
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AMD Collaboration Pathways Investigation alongside AMD-aligned thinking for enterprise and edge AI deployment.
What this work focuses on
Inference engine architecture, workload-specific optimisation, hardware-software alignment, and commercially practical deployment pathways for efficient AI systems.
Why FPGA is relevant
FPGA offers the potential for configurable acceleration, targeted optimisation, and adaptable support for specialised inference scenarios.
