Neural network simulation remains a computationally intensive task. Although the underlying computations, typically multiply$-$accumulates, are straightforward, they are numerous. Consider a basic artificial neural network $($ANN$)$ model where most nodes are interconnected, resulting in O$($N$2)$ connections. For instance, a network with $100,000$ nodes, relatively modest in biological terms, would entail around $10$ billion connections, each requiring a multiply$-$accumulate operation. If a state$-$of$-$the$-$art workstation can handle approximately $10$ million connections per second, then a single pass through the network would take about $1000$ seconds $($approximately $20$ minutes$).$ This rate is insufficient for real$-$time applications like process control or speech recognition, which necessitate updates several times per second. Clearly, a significant challenge exists. This performance bottleneck worsens if each connection demands more complex computations, such as those needed for incremental learning algorithms or realistic biological simulations.

Addressing this computational barrier has spurred extensive research into developing custom Very Large Scale Integration $($VLSI$)$ silicon chips optimized for ANNs. Such chips could potentially accelerate ANN simulations hundreds to thousands of times compared to workstations or personal computers, at a similar cost. The investigation into VLSI chips for neural networks and pattern recognition hinges on the notion that tailoring the chip architecture to the problem's computational characteristics enables the creation of a silicon device offering substantial improvements in performance$-$to$-$cost ratio or "operations per dollar."

In silicon design, a chip's cost primarily depends on its two$-$dimensional area, with smaller chips being cheaper. Within a chip, the cost of an operation is roughly proportional to the silicon area required to execute it$.$ Moreover, speed and cost generally exhibit an inverse relationship: faster chips tend to be larger. Thus, the silicon designer aims to enhance the number of operations per unit area of silicon, known as functional density, consequently boosting operations per dollar. A significant advantage of ANN, pattern recognition, and image processing algorithms is their utilization of simple, low$-$precision operations that demand minimal silicon area. Consequently, chips engineered for ANN emulation can achieve a higher functional density than traditional chips like microprocessors. The impetus for developing specialized chips, whether analog or digital, lies in the potential to enhance performance, reduce costs, or both.

However, the designer of specialized silicon faces various choices and trade$-$offs. One crucial consideration is the balance between flexibility and speed. At one end of the flexibility spectrum, a designer prioritizes speed over versatility, resulting in a fast chip dedicated to a single task. At the opposite end, the sacrifice is speed in favor of programmability, yielding a slower but more adaptable device. Choosing between these traits is challenging since both are desirable. Real$-$world neural network applications ultimately necessitate chips spanning the entire flexibility spectrum.

Suggest a suitable VLSI architecture that can support ANN computations. The report should be mandatorily reviewed by your mentor with his signature and comments mentioned in the report.