Where The Rubber Hits The Road: Implementing Machine Learning On Silicon


Machine learning (ML) is everywhere these days. The common thread between advanced driver-assistance systems (ADAS) vision applications in our cars and the voice (and now facial) recognition applications in our phones is that ML algorithms are doing the heavy lifting, or more accurately, the inferencing. In fact, neural networks (NN) can even be used in application spaces such as file compressi... » read more

Mobile Machine Learning Hardware At Arm


Machine learning is playing an increasingly significant role in emerging mobile application domains such as AR/VR, ADAS, etc. Accordingly, hardware architects have designed customized hardware for machine learning algorithms, especially neural networks, to improve compute efficiency. However, machine learning is typically just one processing stage in complex end-to-end applications, which invol... » read more

Extending Cloud To The Network Edge


The adoption of multi-gigabit networks and planned roll-out of next generation 5G networks will continue to create greater available network bandwidth as more and more computing and storage services get funneled to the cloud. Increasingly, applications running on IoT and mobile devices connected to the network are becoming more intelligent and compute-intensive. However, with so many resources ... » read more

Designing 5G Chips


5G is the wireless technology of the future, and it’s coming fast. The technology boasts very high-speed data transfer rates, much lower latency than 4G LTE, and the ability to handle significantly higher densities of devices per cell site. In short, it is the best technology for the massive amount of data that will be generated by sensors in cars, IoT devices, and a growing list of next-g... » read more

Navigating The Foggy Edge Of Computing


The National Institute of Standards and Technology (NIST) defines fog computing as a horizontal, physical or virtual resource paradigm that resides between smart end-devices and traditional cloud or data centers. This model supports vertically-isolated, latency-sensitive applications by providing ubiquitous, scalable, layered, federated and distributed computing, storage and network connecti... » read more

AI Signals A New Change Of Perspective


A very long time ago, I was a student at MIT, programming with card decks in APL on IBM mainframes and studying AI in a class from Patrick Winston (who took over MIT’s AI lab from the legendary Marvin Minsky). I kept the text book as a reminder of where the world would go. Over four titanic shifts, mainframes/card decks became VAX/VT100, thence to IBM PCs and PC clients tied by Ethernet to co... » read more

Securing IoT Edge Devices


It certainly isn’t any secret that the industry continues to be challenged when it comes to adopting and implementing practical IoT security solutions. However, it is important to understand that IoT edge devices typically only have basic resources, such as reduced CPU processing power and a minimal amount of RAM and flash memory. This means there are limited compute capabilities available fo... » read more

Embedded FPGA: Increasing Security In Next-Gen Networks


The pull of data toward real-time applications on the network’s edge makes the outflow of processing from the cloud inevitable. Programmable logic provides the ability to make computing much more data-centric. While traditional processors demand data to be fed to their pipelines through a complex hierarchy of memory caches, programmable logic makes it possible to construct data pipelines. Dat... » read more

Predictions: Markets And Drivers


Semiconductor Engineering received a record number of predictions this year. Some of them are just wishful thinking, but many are a lot more thoughtful and project what needs to happen for various markets or products to become successful. Those far reaching predictions may not fully happen within 2018, but we give everyone the chance to note the progress made towards their predictions at the en... » read more