The rapid adoption of sophisticated artificial intelligence/machine learning (AI/ML) applications and the shift to cloud-based workloads has significantly increased network traffic in recent years. Historically, the intensive use of virtualization ensured that server compute capacity adequately met the need of heavy workloads. This was achieved by dividing or partitioning a single (physical) server into multiple virtual servers to intelligently extend and optimize utilization. However, this paradigm can no longer keep up with the AI/ML applications and cloud-based workloads that are quickly outpacing server compute capacity.
Accelerating AI And ML Applications With PCIe 5
What Makes Secure Processors Different?
Given the magnificent complexity of modern microprocessors, it’s inevitable that they’ll have bugs and security holes. It might even be physically impossible to create a bug-free CPU, but that’s a mathematics/physics/EDA/statistics/philosophical conundrum that’s above my paygrade. For now, we finds the bugs and we works around ’em.
Memory subsystem solution for next-generation AI training chip
Rambus has announced that Enflame (Suiyuan) Technology has selected Rambus HBM2 PHY and Memory Controller IP for its next-generation AI training chip. Rambus memory interface IP enables the development of high-performance, next-generation hardware for leading-edge AI applications.
California’s IoT Law Is A Good Start, But More Needs To Be Done
Passed by former California governor Jerry Brown, cybersecurity law SB-327 went into effect on Jan. 1. This proactive legislation requires manufacturers to equip IoT devices with “reasonable” security features to prevent unauthorized access, modification and data leaks. Specifically, SB-327 requires manufacturers to implement a unique preprogrammed (default) password for each device. Additionally, manufacturers must ensure that users create a new password the first time a device is activated. Together, these steps are expected to help protect California consumers, as hackers are known to routinely target vulnerable devices shipped with generic or default login credentials.
Implementing Strong Security for AI/ML Accelerators [Part One]
Dedicated accelerator hardware for artificial intelligence and machine learning algorithms are increasingly prevalent in data centers and endpoint devices. These accelerators handle important data which has value and must be protected. Many security threats exist that can compromise these assets. Fortunately, there are security techniques which can mitigate these threats.
Implementing Strong Security for AI/ML Accelerators [Part Two]
Dedicated accelerator hardware for artificial intelligence and machine learning algorithms are increasingly prevalent in data centers and endpoint devices. These accelerators handle important data which has value and must be protected. Many security threats exist that can compromise these assets. Fortunately, there are security techniques which can mitigate these threats.
