Novel Global Interconnect structure with improved Elmore Delay Estimation for Low-Power Applications

With advancements in technology, size and speed have been the important facet in VLSI interconnects. Interconnects are known as the basic building block that provide a connection between two or more blocks and have scaling problems that an IC designer faces while designing. As scaling increases, the impact of interconnect in the VLSI circuits became even more important. It controls all the important electrical characteristics on the chip. With scale-down technology, interconnects not only become closer with each other but their dimensions also change which can directly impact the circuit parameters. Certain RC structures have already been defined to control these parameters but in this paper, authors have proposed a new interconnect structure with improved Elmore delay estimation to reduce delay and power consumption in lumped and distributed interconnect circuits using Pulse and Ramp inputs. Further, the proposed model is estimated and verified theoretically. The linear relationship of power consumption and delay for the RC structure has been observed. The proposed structure with improved Elmore delay estimation shows improvement in delay by 64.25% in lumped circuits and 68.75% in distributed circuits in comparison to existing Elmore delay calculations which help in increasing the overall speed of the interconnect circuit.

Medial entorhinal cortex activates in a traveling wave in the rat

Traveling waves are hypothesized to support the long-range coordination of anatomically distributed circuits. Whether separate strongly interacting circuits exhibit traveling waves remains unknown. The hippocampus exhibits traveling ‘theta’ waves and interacts strongly with the medial entorhinal cortex (MEC). To determine whether the MEC also activates in a traveling wave, we performed extracellular recordings of local field potentials (LFP) and multi-unit activity along the MEC. These recordings revealed progressive phase shifts in activity, indicating that the MEC also activates in a traveling wave. Variation in theta waveform along the region, generated by gradients in local physiology, contributed to the observed phase shifts. Removing waveform-related phase shifts left significant residual phase shifts. The residual phase shifts covaried with theta frequency in a manner consistent with those generated by weakly coupled oscillators. These results show that the coordination of anatomically distributed circuits could be enabled by traveling waves but reveal heterogeneity in the mechanisms generating those waves.

A framework for implementing metaheuristic algorithms using intercellular communication

ABSTRACTMetaheuristic procedures (MH) have been a trend driving Artificial Intelligence (AI) researchers for the past 50 years. A variety of tools and applications (not only in Computer Science) stem from these techniques. Also, MH frequently rely on evolution, a trademark process involved in cell colony growth. Generally, MH are used to approximate the solution to difficult problems but require a large amount of computational resources. Cell colonies harboring synthetic distributed circuits using intercell communication offer a direction for tackling this problem, as they process information in a massively parallel fashion. In this work, we propose a framework that maps MH elements to synthetic circuits in growing cell colonies. The framework relies on cell-cell communication mechanisms such as quorum sensing (QS) and bacterial conjugation. As a proof-of-concept, we also implemented the workflow associated to the framework, and tested the execution of two specific MH (Genetic Algorithms and Simulated Annealing) encoded as synthetic circuits on the gro simulator. Furthermore, we show an example of how our framework can be extended by implementing another kind of computational model: The Cellular Automaton. This work seeks to lay the foundations of mappings for implementing AI algorithms in a general manner using Synthetic Biology constructs in cell colonies.

Scaling principles of distributed circuits

Distributed circuits like the olfactory cortex, hippocampus, and cerebellum contain sub-circuits whose inputs distribute their axons over the entire circuit creating a puzzle of how information is encoded. One method for approaching the puzzle is to view them as scalable systems. In scalable systems the quantitative relationship between circuit components is conserved across brain sizes, and by mapping circuit size to functional abilities - e.g. visual acuity in the visual circuit - scientists have explained information encoding. This approach has not been applied to anti-map circuits as their scalability is unknown. To address this gap in knowledge, we obtained quantitative descriptions of the olfactory bulb and piriform cortex in six mammals using stereology techniques and light microscopy. We found that the olfactory circuit is scalable as it satisfies three requirements of scalable systems. First, quantitative relationships between circuit components are conserved: the number piriform neurons n scales with bulb glomeruli g as n ∼ g3/2. Second, the olfactory circuit has an invariant property: the average number of synapses between a bulb glomerulus and piriform neuron is one. Third, the olfactory circuit is symmorphic, i.e. olfactory ability improves with circuit size. Other distributed circuits with similar properties might also be scalable.