Optimizing Performance with Multi‑Chip Inference Architectures

The past two years have seen a flurry of innovation in inference chip design. While new solutions appear almost weekly, many companies struggle to compare them because reliable benchmarks are scarce. Until now, vendors have advertised TOPS or TOPS/Watt without detailing models, batch sizes, or operating conditions, and the popular ResNet‑50 benchmark is too simplistic for most real‑world workloads.

What truly matters when measuring an inference chip’s performance are high MAC utilization, low power consumption, and a compact silicon footprint. Once these metrics are understood, the next logical step is to explore the benefits—and pitfalls—of deploying multiple chips in a single design.

Why Multiple Chips Can Deliver Linear Gains

When a chip is engineered for parallelism, scaling to two, four, or more units can, in theory, increase throughput proportionally—much like expanding a one‑lane highway to a multi‑lane corridor. The key is to avoid traffic bottlenecks by choosing the right chip and partitioning the neural network correctly.

Partitioning a Neural Network Across Chips

Neural networks are organized into layers. Each layer consumes the output activations of the previous one. In deep models such as ResNet‑50 (50 layers) or YOLOv3 (over 100 layers), early layers generate the largest activations, which gradually shrink as the network progresses.

To split a model across multiple chips, examine both activation size and the number of Multiply‑Accumulate (MAC) operations per layer. The goal is to balance workload: each chip should handle a comparable number of MACs and similar data volumes. If one chip processes the bulk of the work while the other is idle, overall throughput is capped by the slower unit.

Additionally, the cut point in the model determines the data that must be transferred between chips. Choosing a split where the activation size is minimal reduces inter‑chip bandwidth requirements and latency, preventing the communication link from becoming a performance bottleneck.

Illustrative Example: YOLOv3 on Two vs. Four Chips

For YOLOv3 running on a 2‑megapixel image using the Winograd convolution algorithm, the cumulative MAC operations and activation output sizes are illustrated below. A two‑chip configuration typically splits the workload near the 50% MAC mark, resulting in ~1–2 MB activations passed between chips. A four‑chip setup would place cuts at roughly 25%, 50%, and 75%, with the earliest cut often requiring 4–8 MB of data transfer.

Click here for larger image

Activation output size (blue bars) and cumulative MAC operations (red line) for YOLOv3/Winograd/2Mpixel images, illustrating workload distribution across multiple chips (Image: Flex Logix)

Leveraging Performance Modeling Tools

Modern performance tools can now model both single‑chip and multi‑chip configurations. They account for per‑layer MAC counts, activation sizes, and the impact of data transfer bandwidth. Accurate modeling is essential because insufficient inter‑chip bandwidth can negate the theoretical speedup.

For a four‑chip pipeline, bandwidth demands increase because the early layers—where activations are largest—must be shared among all units. While higher bandwidth supports more chips, it also adds overhead that every chip must carry, even if deployed individually.

Conclusion

Deploying multiple inference chips can unlock significant performance improvements, but only when the neural network is partitioned intelligently and the chosen chip architecture supports high MAC utilization and efficient data movement. Prioritize throughput over headline benchmarks like TOPS or ResNet‑50 scores, and design the network to match the chip’s strengths. With the right strategy, you can transform a single‑lane inference pipeline into a high‑capacity, low‑latency system.

— Geoff Tate, CEO of Flex Logix