Photonic Computing Operates Entirely In Memory

For decades, the most elusive goal in artificial intelligence has been to reproduce one of the brain’s defining tricks: computing where information is stored instead of constantly shuttling data between separate memory and processing units. A new photonic computing architecture called FLARE brings that idea into a large-scale system that combines light-based computation with electronic memory. The researchers behind the system report a monolithic chip containing 7,378 artificial neurons, capable of retaining information for seconds while also responding with the rapid dynamics required for high-speed processing. In a demonstration involving autonomous racing-drone navigation, a ten-core version of the system performed sensing, exploration and adaptation with a reported system-level energy cost of just 61.87 attojoules per operation. The work points toward machines that could sense and interpret their surroundings with far less dependence on conventional digital hardware.

Modern artificial intelligence systems are extraordinarily powerful, but much of their energy and time can be consumed moving data rather than performing calculations. In conventional computing architectures, sensory inputs and intermediate results must travel repeatedly between processors and external memory. This separation is especially costly for neural networks, which perform large numbers of mathematical operations on streams of data and often need to preserve information about what happened moments or even minutes earlier. The human brain takes a different approach. Neurons both process signals and maintain internal states, allowing computation and memory to coexist across densely interconnected networks. FLARE, short for a fully in-memory photonic computing architecture, is designed around a similar principle: sensing, processing and memory are integrated into the same computing fabric rather than treated as isolated stages.

The system combines photonic and electronic mechanisms to create what the researchers describe as reconfigurable photonic neurons. Photonic computing uses light to carry and manipulate information, an approach that can exploit the speed and parallelism of optical signals. Electronic components, meanwhile, can provide storage, control and nonlinear behavior that are difficult to implement efficiently with light alone. In FLARE, these mechanisms are coupled so that the artificial neurons can respond to incoming signals, transform them through a deep nonlinear neural network and preserve aspects of their previous activity. That memory is not a single, uniform function. The architecture supports both long-term and short-term dynamics, allowing it to retain learned or accumulated information while continuing to react quickly to new inputs.

This distinction between memory timescales is central to systems that must operate in changing environments. Short-term dynamics can help a machine interpret rapidly evolving signals, such as motion, acceleration or changing visual information. Long-term retention can preserve a more persistent internal state, allowing the system to use information gathered earlier rather than treating every new signal as an isolated event. The FLARE chip reportedly maintained long-term memory for 7.45 seconds while preserving short-term behavior in the gigahertz range. A gigahertz corresponds to a billion cycles per second, so the result represents a combination of sustained memory and extremely rapid signal dynamics. Rather than forcing a system to choose between storing information and processing it quickly, the architecture is intended to make both behaviors available within the same neural substrate.

At the physical level, the promise of photonic computing comes from the way light can propagate and interact across many channels at once. Optical signals can encode information in properties such as intensity, and integrated photonic circuits can perform transformations as light travels through carefully engineered structures. In a neural network, these transformations can represent the weighted combinations of inputs that form the basis of artificial-neuron computation. The critical challenge is that useful intelligence requires more than fast linear operations. Neural systems must also incorporate nonlinear responses, memory and adaptation. FLARE addresses those demands by combining optical processing with electronic mechanisms, creating neurons whose responses can be reconfigured and whose internal state can evolve over time. The result is not simply an optical accelerator attached to a conventional processor, but an attempt to merge the roles of memory, computation and sensing.

Scale is another important feature of the reported demonstration. The researchers built a monolithic multicore chip with 7,378 neurons, then arranged ten cores into a multilayer FLARE system for the autonomous-navigation experiment. A monolithic design places the relevant elements within a unified chip architecture, a step intended to reduce the bottlenecks that arise when separate components must exchange data. Multiple cores and layers allow the network to expand beyond a small laboratory proof of concept and to process information through a deeper neural structure. The architecture’s reconfigurability is also significant: a system designed to operate in the physical world must adjust to different inputs and tasks rather than relying on a fixed, one-purpose circuit. By integrating memory directly into the neural elements, FLARE is designed to keep intermediate activations close to where they are generated, limiting the costly movement of data through external digital memory.

The researchers tested the architecture in a setting that demands continuous interaction between perception and action: autonomous racing-drone navigation. A racing drone must interpret sensory information, explore its environment and adapt its behavior while moving rapidly through space. Conventional systems often divide these tasks among sensors, processors, memory and control units, creating delays and energy costs as information is repeatedly converted, transferred and processed. In the FLARE demonstration, the multilayer photonic system handled sensing, exploration and adaptation as parts of an integrated neural process. Its reported energy cost of 61.87 attojoules per operation illustrates the potential efficiency of performing computation in place. An attojoule is 10^-18 joules, an extraordinarily small unit of energy. The figure is reported at the system level, making it relevant to the complete demonstrated architecture rather than only to an isolated optical operation.

The most striking implication is that FLARE could help shift artificial intelligence away from the traditional sequence of “sense, store, compute and act.” In embodied systems such as drones, robots and autonomous vehicles, intelligence is inseparable from the physical world. Sensors generate continuous streams of data, and useful decisions depend on temporal context: what the machine detected earlier, how the environment is changing and whether a previous action produced the expected result. A network with integrated memory can preserve this context as part of its ongoing activity. A network that also processes signals photonicly could, in principle, handle high data rates without sending every intermediate result to a distant electronic memory. The combination may be particularly valuable where size, energy consumption and response time are tightly constrained.

The work nevertheless represents a pathway rather than a finished replacement for conventional artificial intelligence hardware. The reported results establish the architecture’s neuron count, memory retention, high-speed dynamics and autonomous-navigation demonstration, but practical deployment will depend on how such systems perform across a broader range of tasks and operating conditions. Photonic and electronic components must remain precisely coordinated, and large integrated neural systems must be manufactured, programmed and calibrated reliably. Memory that lasts several seconds may be useful for navigation, but different applications could require other timescales. Likewise, energy per operation is only one part of a system’s total cost; sensing, communication, control and training procedures also matter. These are engineering questions that will shape whether fully in-memory photonic systems can move from specialized demonstrations into everyday machines.

FLARE’s significance lies in the way it brings several long-standing ambitions together on one platform. It uses light for fast, parallel signal processing, electronics for memory and control, neural-network organization for nonlinear computation, and integrated architecture for direct interaction with sensory inputs. The resulting chip does not merely imitate the brain’s appearance; it targets a functional property that makes biological intelligence efficient: memory and computation are deeply intertwined. With thousands of photonic neurons, seconds-long retention, gigahertz-scale short-term dynamics and a low reported energy cost in drone navigation, the system offers a glimpse of machines that compute less by moving data and more by transforming information where it already exists. If the approach continues to scale, future autonomous devices could become faster, more adaptive and substantially more energy-efficient without relying on the memory bottlenecks that define much of today’s AI hardware.