We present FlexiFlow, a lifetime-aware design framework for item-level intelligence (ILI) where computation is integrated directly into disposable products like food packaging and medical patches. Our framework leverages natively flexible electronics which offer significantly lower costs than silicon but are limited to kHz speeds and several thousands of gates. Our insight is that unlike traditional computing with more uniform deployment patterns, ILI applications exhibit 1000× variation in operational lifetime, fundamentally changing optimal architectural design decisions when considering trillion-item deployment scales. To enable holistic design and optimization, we model the trade-offs between embodied carbon footprint and operational carbon footprint based on application-specific lifetimes. The framework includes: (1) FlexiBench, a workload suite targeting sustainability applications from spoilage detection to health monitoring; (2) FlexiBits, area-optimized RISC-V cores with 1/4/8-bit datapaths achieving 2.65× to 3.50× better energy efficiency per workload execution; and (3) a carbon-aware model that selects optimal architectures based on deployment characteristics. We show that lifetime-aware microarchitectural design can reduce carbon footprint by 1.62×, while algorithmic decisions can reduce carbon footprint by 14.5×. We validate our approach through the first tape-out using a PDK for flexible electronics with fully open-source tools, achieving 30.9\,kHz operation. FlexiFlow enables exploration of computing at the Extreme Edge where conventional design methodologies must be reevaluated to account for new constraints and considerations. FlexiFlow is available at https://github.com/harvard-edge/FlexiFlow.

In this paper we describe our initiative to incorporate research, professional practice and methodologies into two undergraduate software engineering courses. As part of the Undergraduate Research Experience (URE) initiative on our campus, we modified two of our core software engineering courses. In our Software Development II (junior level) and Software Development Project (senior level) courses we integrated research projects, corporate visits and research talks to develop a deeper understanding of software engineering research and practice amongst our students.

Nodes within existing P2P networks typically exchange periodic keep-alive messages in order to maintain network connections between neighbours. This paper investigates a number of algorithms which allow each individual connections to extend the interval between successive keep-alive messages based u

One of the primary concerns of document analysis systems is logo or trademark recognition, but few solutions proposed to date can deal with the problem of successfully classifying a logo that has been distorted in scale or rotation. We propose the use of a two-stage method applying a generalised regression neural network to provide the necessary flexibility to cope with these variations. A novel method of tiling which increases classification accuracy is also presented. The issues of scale and rotation are discussed in relation to the network's interpolation capability, as well as several other points effecting overall accuracy.