Argon2 and energy consumption

The memory-intensive nature of computing is what renders ASICs less effective for mining. The algorithm's difficulty level is adjusted in response to the competitive environment of the network, linking the challenge of mining more directly to memory allocation rather than sheer processing power. This adaptive difficulty mechanism allows for a separation from energy consumption.

The Argon2 algorithm, utilized in our system, decreases power usage as the difficulty level increases by leveraging memory for mining tasks. Memory components inherently consume fewer watts compared to traditional processing units. As the mining difficulty escalates, the algorithm demands more memory, causing the mining process to become constrained by the capacity for memory input and output. This slowdown in processing speed results in progressively lower power consumption, as slower operations require less energy.

As the mining difficulty increases, individual GPU utilisation decreases and in turn watt usage. Computations is being offloaded into memory instead. Watt goes up for memory but its using 1/300 of wattage as the GPU. GPU utilization remains constant (GPU %), but reduced hashing intensity leads to lower power consumption. Memory uses less energy than GPUs, and as mining difficulty increases, it reduces power consumption, preventing an increase in overall energy use. Consequently, the overall energy footprint of the mining process is substantially reduced, offering a more energy-efficient approach to mining. Currently, it's not feasible to increase the total energy consumption across the network because as the mining difficulty escalates, it inherently reduces energy usage. Energy efficiency is a crucial consideration in the context of Proof of Work (PoW) mining, and it's essential to recognise that not all PoW mining can be painted with the same brush in terms of energy expenditure. This distinction is particularly evident when comparing our approach to traditional methods.

The SHA-256 algorithm, which underpins Bitcoin mining, is an older algorithm not designed with energy efficiency in mind. As the difficulty of mining on the Bitcoin blockchain increases, the financial outlay required for energy consumption escalates, prompting miners to operate as many machines as possible to maximize economic returns and rewards. This scenario sets off a competitive rush to discover blocks and secure rewards.

However, this approach results in a continuous increase in energy consumption because the SHA-256 algorithm lacks mechanisms for self-optimization. Being a basic algorithm developed about 40 years ago by the U.S. government, it does not incorporate modern energy-saving technologies or methods, leading to inherently higher energy use with no capacity for reduction. In contrast, the methodology we're discussing aims to adjust energy consumption dynamically, making it fundamentally different and more energy-efficient.