Physical AIartificial intelligenceAI stands for artificial intelligence shifts the focus of system design away from compute alone. When devices must see, interpret, decide, and act in real time, memory behavior becomes just as important as accelerator performance. The critical issue is no longer only how fast an NPUNeural Processing UnitA processor optimized for neural-network inference workloads., CPUcentral processing unitMain processor that executes program instructions and performs data operations., DSP1. differential signal processingSignal processing based on differences between two signals, often rejecting common interference.2. digital signal processing, or ISP can process data, but whether the right data reaches the right engine at the right time.
That makes data movement a first-class architectural concern. A physical AIartificial intelligenceAI stands for artificial intelligence device may have to manage sensor streams, model weights, activations, temporary buffers, control loops, and safety functions at the same time. If memory access becomes unpredictable, compute engines can stall. Excess transfers also increase power consumption, while poor buffering can create latency that is difficult to recover later.
The memory hierarchy in these systems typically combines on-chip SRAM1. static random access memoryMemory technology or system for temporarily or permanently storing digital information.2. static random-access memorySRAM is used here as a test vehicle for embedded memory performance. and cache with larger external LPDDR or GDDR. External memory holds model weights, sensor data, activations, and other working data, while on-chip storage keeps the portions needed most urgently close to compute. Reusing local data reduces latency and energy, but on-chip storage is limited because SRAM1. static random access memoryMemory technology or system for temporarily or permanently storing digital information.2. static random-access memorySRAM is used here as a test vehicle for embedded memory performance. and cache consume silicon area.
That tradeoff is especially visible in real devices such as humanoid robots, drones, autonomous vehicles, smart cameras, inspection robots, industrial robots, and autonomous mobile robots. Each of these platforms combines local intelligence with a different mix of vision, sensing, control, and communication workloads, and each stresses memory and interfaces in a different way.
Humanoids, for example, can combine cameras, depth sensing, audio, inertial data, balance control, object recognition, path planning, and actuator feedback within a tight power and thermal envelope. If perception data arrives late, control decisions are delayed. Repeatedly reloading model weights and intermediate data also raises power use and can reduce sustained performance.
Other platforms face similar constraints in different forms. Drones and autonomous mobile robots need visual navigation, obstacle avoidance, stabilization, object tracking, wireless communication, and video processing while staying within power and thermal budgets. Unnecessary external-memory traffic shortens operating time, adds heat, and introduces latency into the control loop. In these systems, bandwidth per watt matters as much as raw bandwidth.
Autonomous vehicles and advanced driver assistance systems create an even more demanding environment because cameras, radar, lidar, ultrasonic sensors, vehicle telemetry, map data, and control functions all compete for memory access. Some streams require continuous throughput, while others depend on fusion across sensor domains. In that setting, memory behavior affects both throughput and response predictability.
The preferred memory choice therefore depends on the workload. LPDDR is often a strong fit when bandwidth per watt and memory capacity are needed for local inference. GDDR becomes attractive when sustained image or sensor-stream bandwidth is the dominant requirement. Cadence offers LPDDR6/LPDDR5X PHYphysical layer interfacePHY denotes the physical-layer interface of a data link. and Controller IP1. image placement2. Internet protocolInterface or communications concept for transmitting data between devices and systems.3. Intellectual PropertyIP denotes licensable semiconductor design blocks or technology elements. supporting data rates up to 14.4 Gbps, and GDDR7 PHYphysical layer interfacePHY denotes the physical-layer interface of a data link. IP1. image placement2. Internet protocolInterface or communications concept for transmitting data between devices and systems.3. Intellectual PropertyIP denotes licensable semiconductor design blocks or technology elements. supporting data rates up to 36 Gbps. The central design goal is to right-size the full memory hierarchy around data locality, traffic contention, buffering, latency predictability, and operating constraints.