Balancing power and experience
by Kevin McIntyre, IEM Product Manager, ARM Holdings
As expectations for consumer electronics products increase, manufacturers must begin to address the important issue of battery life. If the industry does not meet the consumer’s expectations it risks slowing down its growth.
Consumer Electronics device manufacturers are under constant pressure to produce feature rich products with both high performance and that are power efficient. Consumers now expect a standard mobile phone to have a high resolution screen, 3D gaming capabilities, an MP3 player and still offer a substantial battery life.
These device designers now face the challenge of improving battery life to support these new features, with two clear routes to meeting these requirements; either improve the energy density in batteries, or add new energy management techniques to existing power management frameworks. Although each option is a valid means of boosting battery life, it is the balance of both technologies that will lead to the best commercial solution.
Click here to read more ...
Related Semiconductor IP
- LPDDR6/5X/5 PHY V2 - Intel 18A-P
- ML-KEM Key Encapsulation & ML-DSA Digital Signature Engine
- MIPI SoundWire I3S Peripheral IP
- ML-DSA Digital Signature Engine
- P1619 / 802.1ae (MACSec) GCM/XTS/CBC-AES Core
Related White Papers
- Balancing Power and Performance With Task Dependencies in Multi-Core Systems
- eFPGAs Bring a 10X Advantage in Power and Cost
- How Low Can You Go? Pushing the Limits of Transistors - Deep Low Voltage Enablement of Embedded Memories and Logic Libraries to Achieve Extreme Low Power
- High Speed, Low Power and Flexibility Drive DisplayPort's Increasing Popularity
Latest White Papers
- AnaFlow: Agentic LLM-based Workflow for Reasoning-Driven Explainable and Sample-Efficient Analog Circuit Sizing
- FeNN-DMA: A RISC-V SoC for SNN acceleration
- Multimodal Chip Physical Design Engineer Assistant
- An AUTOSAR-Aligned Architectural Study of Vulnerabilities in Automotive SoC Software
- Attack on a PUF-based Secure Binary Neural Network