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The rapid growth of deep learning has brought neural networks into countless fields, from natural language processing and video analysis to planning, sentiment detection, and computer vision. With this expansion comes increasing specialization, as models become more complex and diverse. For hardware designers, the challenge is clear: how can accelerators deliver top-tier performance today while staying adaptable to the demands of tomorrow’s neural networks?
The Balancing Act in Accelerator Design
Developing neural network accelerators requires walking a tightrope. On one hand, chips must achieve extreme computational throughput to handle tasks in industries such as mobile devices, automotive systems, data centers, and embedded platforms. On the other, they must remain flexible enough to adapt to the rapidly evolving landscape of AI models. Efficiency constraints around power, area, and bandwidth make this balancing act even more demanding.
Imagination’s Series4 neural network accelerators (NNAs) were built with this challenge in mind. At their core, they can deliver up to 40 trillion operations per second (TOPS) while maintaining impressive area and energy efficiency. Specialized hardware modules—optimized for operations like convolutions, activations, and pooling—enable this raw performance. Yet specialization alone isn’t enough. Modern networks increasingly rely on operations such as attention layers and non-maximum suppression, which don’t map cleanly onto fixed-function hardware.
Why Not Just Add More Hardware?
One option would be to add dedicated hardware for each new operation. While this might solve specific performance gaps, it comes with major drawbacks. Rarely used modules consume valuable silicon area and power, creating “dark silicon” that sits idle most of the time. Worse still, this approach locks hardware into supporting only known techniques, leaving it perpetually one step behind the state of the art.
The alternative is to make accelerators more programmable, following a RISC-like philosophy that shifts complexity into software. This adds flexibility but often sacrifices density, driving up power and area requirements. Neither extreme delivers the ideal balance of efficiency and adaptability.
The ROSC Philosophy
To break this deadlock, Imagination developed a new design strategy called Reduced Operation Set Computing (ROSC). The idea is simple but powerful: instead of adding endless specialized units or diluting efficiency with general-purpose designs, ROSC leverages a small set of high-performance hardware modules and reconfigures them to emulate more complex operations.
In practice, this means that even when modules are used outside their primary purpose—leading to lower utilization—the sheer compute capacity of the Series4 still delivers excellent performance. For example, running a challenging operation at just 1% utilization can still achieve hundreds of billions of operations per second, making it faster and more resource-efficient than offloading to a CPU or GPU.
A key enabler of ROSC is an advanced on-chip memory system that supports tensor tiling. This allows data to be reused across multiple hardware passes, minimizing overhead when complex operations are broken down into computational subgraphs of simpler steps.
Rethinking What Hardware Can Do
ROSC challenges the notion that a “convolution engine” is only for convolutions or that a “pooling unit” is limited to pooling. With the right compiler strategies, these modules can be reconfigured to perform a surprisingly wide variety of tasks. Advanced graph-lowering techniques allow nearly any modern neural network operation to be expressed as a combination of Series4’s modules.
Take the softmax function, for instance. While it has no dedicated hardware in Series4, ROSC decomposes it into a sequence of supported operations—such as reductions, reciprocal calculations, and exponential lookups—that together replicate its behavior. What might seem like a complicated, specialized function is instead executed through clever reuse of existing modules.
Similarly, three-dimensional convolutions, which are not natively supported, can be built from sequences of two-dimensional convolutions combined with elementwise additions. This approach extends naturally to higher-dimensional operations, demonstrating how software techniques complement the hardware to expand its applicability.
Why ROSC Matters
The brilliance of ROSC lies in its ability to combine high efficiency with adaptability. Specialized modules ensure that common operations like convolutions run at blistering speeds, while flexible compilation techniques allow the same hardware to handle more complex and less frequent tasks. This dual capability keeps hardware relevant for current applications while making it resilient to the unknown demands of future AI models.
Conclusion
Designing neural network accelerators has always meant choosing between performance and flexibility. Reduced Operation Set Computing shows that it doesn’t have to be a binary choice. By maximizing reuse of specialized hardware through smart reconfiguration, ROSC delivers both the computational density needed for today’s workloads and the adaptability required for tomorrow’s innovations. In a field evolving as quickly as AI, that balance may prove to be the ultimate competitive edge.
