Low-latency power management in edge AI architectures
Given that AI workloads increase power consumption in edge devices, can traditional power management strategies still be effective? This article examines whether techniques such as dynamic voltage and frequency scaling (DVFS) and rapid sleep modes can still be useful when millisecond-level response times are required.
As we discuss, the implementation of AI-focused NPUs varies by manufacturer, with respect to low-power sleep modes, so assessing how and if sleep modes are available is also a factor. We also explore cognitive power management, In this emerging approach, AI helps to optimize power usage by dynamically adjusting allocation based on what the system is doing and its current state. Smart techniques like these can help recover energy in systems that need to maintain real-time performance without exceeding their overall power budget.
The hidden challenge of edge AI
Can embedded devices afford to sleep between inference cycles without breaking timing requirements? And does cranking up system power to speed up inference really maintain real-time performance, or does it just move the problem somewhere else?
The integration of AI at the network edge has fundamentally changed how embedded real-time systems operate. By processing data locally rather than relying on cloud infrastructure, edge AI reduces latency. This could prove to be the critical factor for applications such as autonomous vehicles and industrial automation, where millisecond delays can have serious consequences.
Edge AI also enhances privacy, since sensitive data is confined to the device, rather than transmitted over the network. Moreover, it ensures continued operation even when network connectivity fails.
However, these advantages come with some engineering tradeoffs. The computational demands of neural network inference clash directly with the stringent power budgets of low-power (often battery-operated) and thermally constrained embedded devices.
Engineers must find the right compromise between performance and dynamic power management strategies. Achieving acceptable inference accuracy while staying within milliwatt-scale power envelopes remains one of the field's most pressing challenges, requiring careful co-design of algorithms, architectures and implementation.
Understanding AI power-performance relationships
Edge AI’s (low) power-performance dilemma relates to inference workloads. Traditional computing tasks exhibit relatively steady power consumption. AI inference at the edge creates highly variable demand patterns. The processor might switch rapidly from handling light processing tasks (such as diagnostics), consuming minimal power, to executing intensive matrix multiplications for a neural network layer.
The power demand could change from idling to maximum delivery in little more than a clock cycle. This variability creates the dilemma. Designing the power system to constantly deliver peak power will waste a significant amount of energy and generate excessive heat.
Designing too conservatively means the processor could be starved during compute-intensive moments, leading to performance degradation or system instability. AI-centric systems require power management that can track, adapt and respond to these fluctuating loads with more speed and greater precision than previously demanded.
Dynamic scaling in real-time edge AI systems
Dynamic voltage and frequency scaling (DVFS) have been used in computing for years to balance performance and power consumption. However, edge AI systems push DVFS to new extremes. Traditional DVFS implementations might adjust power states over milliseconds or even seconds. Edge AI applications need transitions to happen in microseconds.
By lowering power dissipation, DVFS also helps manage system temperature, thereby reducing the risk of overheating and improving overall reliability. This approach has been widely adopted by modern processors and embedded systems.
In commonly used CMOS logic ICs, power consumption consists of static and dynamic components, which can be computed as follows:
PDynamic = PLoad + PPowerDissipation
Where:
PLoad = COUT x Vcc2 x F(out)
PPowerDissipation = CIN x Vcc2 x F(in)
Source: Calculating the operating supply current and power dissipation, Toshiba Semiconductor & Storage
DVFS leverages the quadratic dependence of dynamic power on voltage and its linear dependence on frequency by simultaneously lowering both parameters when the processor is not fully utilized. Because the maximum operating frequency scales with the supply voltage, decreasing the frequency permits a corresponding voltage reduction, leading to an overall cubic decrease in dynamic power consumption.
Implementing DVFS demands close coordination between hardware and software. On the hardware side, voltage regulation is managed by power management integrated circuits (PMICs), which supply controlled voltages to processor cores, memory and communication interfaces. Frequency adjustment, in turn, relies on integrated frequency synthesizers, typically phase-locked loop (PLL) circuits, that enable dynamic tuning across a range of output frequencies. On the software side, specialized firmware algorithms monitor workload characteristics and predict upcoming compute demands.
When the system detects an incoming inference request, it must rapidly scale up both voltage and clock frequency to handle the workload. Once the inference completes, it should immediately scale back down to conserve energy. This constant interleaving of scaling up and down happens hundreds or thousands of times per second in active edge AI systems.
The challenge intensifies when you consider that voltage and frequency adjustments cannot happen instantaneously. Power delivery networks have physical limitations, capacitances that must charge and discharge, and inductances that resist rapid current changes. The power management system must account for these physical constraints while still meeting the nanosecond-level timing requirements of real-time AI inference.
The importance of sleep mode transitions
Beyond active power scaling, edge AI systems can benefit from intelligent sleep states. When the AI accelerator is not processing inference requests, it should enter low-power modes as quickly as possible. The time to enter and exit sleep can vary significantly depending on the processor type and the specific sleep mode, ranging from microseconds for low-power states to hundreds of milliseconds for deep sleep.
Sleep modes are employed to reduce power consumption by disabling selected peripherals and clock domains within a device. A dedicated sleep controller manages the transition between active and sleep states, while the number and type of available sleep modes vary by processor architecture.
When the device enters sleep mode, program execution halts. Depending on the specific mode, various subsystems and clock domains are powered down. The device typically wakes on an interrupt, the source of which depends on the sleep configuration.
An interrupt could be triggered by an intelligent peripheral or off-chip logic handling an external event, such as a contactless card approaching an NFC reader. The device must quickly resume operation and execute the interrupt service routine. Execution continues until the executive routine (or real-time operating system) re-enters a sleep mode.
Advanced power management architectures implement what might be called "shallow sleep" states that sacrifice some power savings for dramatically faster wake times. These states keep critical circuits powered and clocks ready to activate, enabling the processor to spring back to full operation in just a few microseconds. The firmware must intelligently decide which sleep state to use based on predicted idle duration, balancing power savings against wake-up latency.
Weighing up the options when selecting an AI microcontroller
An accelerator is typically a dedicated, hardwired addition that can start and stop quickly, with little to no standby power consumption and zero wake time. An NPU may offer various sleep modes, similar to the main core, which provide increasing levels of low power consumption but with corresponding wake times.
Device manufacturers can choose how and if to implement these sleep modes, so the same ‘off the shelf’ NPU may be implemented differently by different device manufacturers, with respect to sleep modes and wake times. Talk to an Avnet expert to discuss your requirements and the options that best meet them.
Cognitive power management: when AI manages AI
An interesting development in this field involves using lightweight AI models to manage power delivery for larger, more complex AI workloads. This approach, called cognitive power management, recognizes that predicting compute loads is itself a pattern recognition problem that machine learning can solve effectively.
A small, efficient neural network analyzes incoming data streams and predicts the computational load of upcoming inference tasks. For instance, an AI-driven artificial vision system that captures, processes and interprets visual data to perform tasks such as inspection, measurement or object recognition can understand whether the next frame of video will require simple background processing or complex object detection. Knowing this allows the power management system to adjust voltage and frequency levels preemptively.
This proactive approach eliminates much of the latency associated with reactive power management, where the system only responds after detecting an increased computational demand.
The value of this approach lies in its efficiency. The predictive model itself consumes minimal power and computational resources, operating continuously in the background without imposing significant overhead on the system. Yet it enables the main AI accelerator to always have the right amount of power available at precisely the moment it is needed, tackling both waste and performance bottlenecks.
What makes cognitive power management particularly attractive is how it addresses the dynamic nature of real-world workloads. Traditional static power allocation strategies either over-provision or under-provision power. Over-provisioning wastes energy during light computation. Under-provisioning forces the system into costly frequency scaling transitions. In contrast, the predictive approach learns the temporal patterns inherent in specific applications.
A surveillance camera processing mostly static scenes with occasional motion events, for instance, develops different prediction patterns than a robot navigating crowded environments. This workload-specific adaptation means the power management overhead pays dividends across diverse deployment scenarios.
Integration and system-level considerations in edge AI
Power management will become increasingly important as edge AI systems become more prevalent and their applications become more demanding. The combination of traditional techniques, such as DVFS and sleep mode management, with cognitive power management algorithms is a big step forward, since it allows for the ultra-low-latency response that AI-enabled applications will need.
Implementing advanced power management for edge AI requires careful system-level integration where hardware, firmware and software work together. The AI accelerator must communicate its power requirements to the management system with minimal latency. The power delivery network must be physically designed to minimize parasitic inductances and resistances that would slow transient response.
Firmware plays a crucial role, continuously monitoring system state, predicting upcoming demands and commanding power adjustments. This firmware needs to be advanced to make smart choices and light enough to run without causing delays. It’s a tough balance that needs a lot of real-world testing and careful adjustment.
Adaptive power management has evolved from being a topic of interest for scientists in laboratories to becoming integral to commercial products. These innovations are already enhancing industrial automation systems, advanced driver assistance systems (including first deployments of autonomous vehicles), and next-generation robotics.
Latest-generation 32-bit microcontrollers, like the RA8P1 from Renesas, come with AI-acceleration, vector processing instruction sets and integrated neural processing units (NPUs). These features help designers to streamline the development of the complex signal processing and machine learning algorithms used in challenging applications, such as face recognition.
The RA8P1 is also manufactured using the comprehensive 22 nm ultra-low leakage process (22ULL) from TSMC. This fits into TSMC’s portfolio of system-on-chip technologies and includes static random-access memory (SRAM) and non-volatile memory.
Engineers can start developing with the RA8P1 today, using Renesas’ RTK7EKA8P1S01001BE development kit.