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AI/ML Hardware Accelerators

Systolic arrays, dataflow architectures, quantization and the memory bottleneck that really limits accelerator performance.

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AI/ML Hardware Accelerators Aug 1, 2026 • 2 min read Quantization and Precision Trade-offs in Hardware Neural networks don't necessarily need full 32-bit floating point precision to produce useful results. Quantization – using lower-precision number formats – is one of the most effective techniques for reducing an accelerator's memory footprint and energy consumption. Read article AI/ML Hardware Accelerators Jul 27, 2026 • 2 min read Scaling AI Accelerators: From Edge to Data Center AI acceleration isn't one-size-fits-all – the design considerations for a battery-powered edge sensor are almost entirely different from those for a data center training cluster, even though both may run similar neural network algorithms. Read article AI/ML Hardware Accelerators Jul 23, 2026 • 2 min read Memory Bottlenecks in AI Accelerators: The Real Constraint Raw compute throughput (TFLOPS) gets most of the attention in AI accelerator marketing, but in practice, memory bandwidth is often the real constraint that determines an accelerator's actual, achieved performance. Read article AI/ML Hardware Accelerators Jul 22, 2026 • 2 min read Dataflow Architectures for Neural Networks How data moves through an accelerator's compute array – its 'dataflow' – is one of the most fundamental architectural choices in AI accelerator design, and it significantly affects both performance and energy efficiency. Read article AI/ML Hardware Accelerators Jul 19, 2026 • 2 min read Systolic Arrays Explained: The Engine Behind Many AI Accelerators Systolic arrays are a widely used architecture for accelerating the matrix multiplications that dominate neural network computation. Their elegant, regular structure makes them both highly efficient and relatively simple to design and verify. Read article

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