# On Implementing Technomorph Biology for Inefficient Computing
## 2 Notions of Computation
### Issues in Formulating Efficiency
\begin{table}
\begin{tabular}{c c c c} \hline
**Item** & **Technical** & **Biological** & **Notes** \\ \hline Computing units & \(10^{12}(*10^{7})\), single complex & \(10^{12}\), simple cooperating & Processor/brain \\  & processors & processors & (supercomputer) \\ \hline No of independent & \(1\) & \(10^{18}\) & Typical bus/axons \\ connections & \(1\) & \(10^{18}\) & Typical bus/axons \\ \hline Versatility & Many operations & One operation & Per trigger \\ \hline Utilization & Almost full & Very low & \\ \hline Power consumption & \(200\) W (\(20\) MW) & \(0.1\) W & [41] (supercomputer) \\ \hline Power utilization & Wasteful & Barely minimal & \\ \hline Cost function & Minimizing gates in the & Minimizing time & Affects power \\  & implementation & differences when & consumption and \\  & & delivering result & operating accuracy \\ \hline Time for elementary & \(10^{-9}\) (fixed) & \(10^{-2}\) (variable) Decreased & \\  & processing, sec & \(1/100\) by cooperation & \\ \hline Theoretical base & Tends to deviate from & & \\  & theory & Only partly known & [21; 42] \\ \hline Time awareness & n/a & Crucial & [42] \\ \hline Operations to imitate the & \(\text{up to }10^{5}\) & up to \(10^{5}\) & Depends on the operation \\ \hline Operand and result type & Mathematically defined, & Physiologically defined; & \\  & exact, perfect & non-entirely deterministic & \\ \hline Parallelization & Parallelized sequential & True parallel (native) & [21] \\ \hline Computing stack & Poor & Perfect & [21] \\ \hline Information type & Digitally described by & Spatially and temporally & \\  & Shannon model & distributed & [7] \\ \hline Communication method & Point-to-point (fast but & Many-to-many (slow but & \\  & serial) & parallel) & [43] \\ \hline Communication time & \(\text{from }10^{-9}\) (secs & & \\  & up to minutes) & 1–10 ms & ANNs and supercomputer \\  & & up to minutes) & [32; 42; 44] \\ \hline Communication wiring & Strongly simplified, sparse, & Unbelievably dense and & \\  & shared & complex, private & \\ \hline Communication scaling & Very poor & Very good & [32; 43; 44] \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of some characteristics of the major computing implementations. The second is how to map these operations to each other given that thousands of machine instructions are needed to emulate an analog integration (diffusion) and thousands of neurons are needed to perform simple digital addition, not to mention the thousands of machine instructions required to connect to the external world [45; 46; 47]; so the question of how to map these operations to each other remains open. In reality, the biological data transmission time is up to two orders of magnitude longer than the computing time. In technical computing, the transmission through a bus might result in similar ratios. Furthermore, the operations can be at least partly parallelized. \"The naive conversion between Operations Per Second (OPS) and Synaptic Operations Per Second (SOPS) as follows: 1 SOPS \(\approx\) 2 OPS\" [48] has limited validity. It can differ from the real one by two to four orders of magnitude in both directions, depending on the context, not to mention cross-comparison between biological and technical implementations. Any conversion reflects the misunderstanding that the total operating time comprises only the computing time, and the data transmission time can be neglected. The third problem we face is the \"non-deterministic\", non-perfect, analog, asynchronous method of operation of the brain versus the deterministic, perfect, digital, synchronous operation of the computer. Furthermore, the arrival time of the received postsynaptic spikes [42] almost entirely determine the input arguments. We also know that the brain has no central clock, instruction pointer, or memory unit; furthermore, it uses mixed data-flow and event control and learns excellently.