Recent years have witnessed a significant increase in the storage density of NAND flash memory, making it a critical component in modern electronic devices. However, with the rise in storage capacity comes an increased likelihood of errors in data storage and retrieval. The growing number of errors poses ongoing challenges for system designers and engineers, in terms of the characterization, modeling, and optimization of NAND-based systems. We present a novel approach for modeling and preventing errors by utilizing the capabilities of generative and unsupervised machine learning methods. As part of our research, we constructed and trained a neural modulator that translates information bits into programming operations on each memory cell in NAND devices. Our modulator, tailored explicitly for flash memory channels, provides a smart writing scheme that reduces programming errors as well as compensates for data degradation over time. Specifically, the modulator is based on an auto-encoder architecture with an additional channel model embedded between the encoder and the decoder. A conditional generative adversarial network (cGAN) was used to construct the channel model. Optimized for the end-of-life work-point, the learned memory system outperforms the prior art by up to 56\% in raw bit error rate (RBER) and extends the lifetime of the flash memory block by up to 25\%.

Recent years have witnessed a significant increase in the storage density of NAND flash memory, making it a critical component in modern electronic devices. However, with the rise in storage capacity comes an increased likelihood of errors in data storage and retrieval. The growing number of errors poses ongoing challenges for system designers and engineers, in terms of the characterization, modeling, and optimization of NAND-based systems. We present a novel approach for modeling and preventing errors by utilizing the capabilities of generative and unsupervised machine learning methods. As part of our research, we constructed and trained a neural modulator that translates information bits into programming operations on each memory cell in NAND devices.

Upcoming certification actions related to the security of machine learning (ML) based systems raise major evaluation challenges that are amplified by the large-scale deployment of models in many hardware platforms. Until recently, most of research works focused on API-based attacks that consider a ML model as a pure algorithmic abstraction. However, new implementation-based threats have been revealed, emphasizing the urgency to propose both practical and simulation-based methods to properly evaluate the robustness of models. A major concern is parameter-based attacks (such as the Bit-Flip Attack - BFA) that highlight the lack of robustness of typical deep neural network models when confronted by accurate and optimal alterations of their internal parameters stored in memory. Setting in a security testing purpose, this work practically reports, for the first time, a successful variant of the BFA on a 32-bit Cortex-M microcontroller using laser fault injection. It is a standard fault injection means for security evaluation, that enables to inject spatially and temporally accurate faults. To avoid unrealistic brute-force strategies, we show how simulations help selecting the most sensitive set of bits from the parameters taking into account the laser fault model.

Like most Internet-of-things devices these days, Amazon's Echo Dot gives users a way to perform a factory reset so that, as the corporate behemoth says, users can "remove any ... personal content from the applicable device(s)" before selling or discarding them. But researchers have recently found that the digital bits that remain on these reset devices can be reassembled to retrieve a wealth of sensitive data, including passwords, locations, authentication tokens, and other things. This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast. Most IoT devices, the Echo Dot included, use NAND-based flash memory to store data.

For many IoT domains, Machine Learning and more particularly Deep Learning brings very efficient solutions to handle complex data and perform challenging and mostly critical tasks. However, the deployment of models in a large variety of devices faces several obstacles related to trust and security. The latest is particularly critical since the demonstrations of severe flaws impacting the integrity, confidentiality and accessibility of neural network models. However, the attack surface of such embedded systems cannot be reduced to abstract flaws but must encompass the physical threats related to the implementation of these models within hardware platforms (e.g., 32-bit microcontrollers). Among physical attacks, Fault Injection Analysis (FIA) are known to be very powerful with a large spectrum of attack vectors. Most importantly, highly focused FIA techniques such as laser beam injection enable very accurate evaluation of the vulnerabilities as well as the robustness of embedded systems. Here, we propose to discuss how laser injection with state-of-the-art equipment, combined with theoretical evidences from Adversarial Machine Learning, highlights worrying threats against the integrity of deep learning inference and claims that join efforts from the theoretical AI and Physical Security communities are a urgent need.

Irvine Calif.-based Syntiant thinks it can use embedded flash memory to greatly reduce the amount of power needed to perform deep-learning computations. Austin, Tex.-based Mythic thinks it can use embedded flash memory to greatly reduce the amount of power needed to perform deep-learning computations. They both might be right. A growing crowd of companies is hoping to deliver chips that accelerate otherwise onerous deep learning applications, and to some degree they all have similarities because "these are solutions that are created by the shape of the problem," explains Mythic founder and CTO Dave Fick. When executed in a CPU, that problem is shaped like a traffic jam of data. A neural network is made up of connections and "weights" that denote how strong those connections are, and having to move those weights around so they can be represented digitally in the right place and time is the major energy expenditure in doing deep learning today.