Skip to content

Frontier Hardware Platforms Guide

SC-NeuroCore supports 31 platform classes spanning every known and speculative compute paradigm — from traditional FPGAs to living organoid co-processors, DNA-perovskite synapses, and trapped-ion quantum neurons.

This guide covers the frontier platform classes, their physical principles, compilation considerations, and code examples.

Table of Contents

  1. Cryogenic and Non-Volatile Platforms
  2. Biological, Electrochemical, and Wafer-Scale Platforms
  3. Memory-Centric and Quantum-Inspired Platforms
  4. Interconnect, Acoustic, Fluidic, and Space Platforms
  5. Sovereign, Organic, and Magnonic Platforms
  6. Adaptive Reliability Platforms
  7. Custom Profile Registration
  8. Platform Class Reference

Cryogenic and Non-Volatile Platforms

Superconducting / Cryogenic (3 profiles)

Physics: Single Flux Quantum (SFQ) logic operates at 4K with Josephson junctions producing picosecond voltage pulses. Clock speeds reach 100+ GHz with near-zero switching energy (~10⁻¹⁹ J per gate).

Profile Vendor Family Width
nist_sfq NIST SFQ 8-bit
northrop_aqfp Northrop Grumman AQFP 8-bit
mit_josephson MIT-LL Josephson-JJ 8-bit

Compilation notes: - Use 8-bit or narrower data widths (gate budgets are small) - Overflow: saturate (no wrap — metastability risk at 4K) - SFQ pulse timing requires careful pipeline depth

Python
from sc_neurocore.compiler.platforms import get_profile

p = get_profile("nist_sfq")
print(f"Class: {p.platform_class}")  # superconducting
print(f"Width: Q{p.data_width - p.fraction}.{p.fraction}")  # Q4.4

Spintronic (2 profiles)

Physics: Spin-Transfer Torque (STT) and Spin-Orbit Torque (SOT) MRAM use electron spin states for non-volatile storage. Enables in-memory computation with ns switching and near-infinite endurance.

Profile Vendor Family Width
everspin_stt Everspin STT-MRAM 8-bit
samsung_sot Samsung SOT-MRAM 8-bit

Key advantage: Non-volatile synaptic weights survive power cycles, enabling instant-on neural inference without weight reload.

Ferroelectric (2 profiles)

Physics: Ferroelectric FETs (FeFET) and FeRAM use polarisation switching in hafnium oxide (HfO₂) for non-volatile CIM with CMOS-compatible process integration.

Profile Vendor Family Width
gf_fefet GlobalFoundries FeFET-22FDX 8-bit
sk_hynix_feram SK Hynix FeRAM 8-bit

CGRA (3 profiles)

Physics: Coarse-Grained Reconfigurable Arrays provide FPGA-like flexibility with ASIC-like efficiency. Reconfiguration happens at the PE (Processing Element) level, not the LUT level.

Profile Vendor Family Width Freq
samsung_cgra Samsung CGRA-v2 16-bit 1 GHz
qualcomm_cgra Qualcomm NPU-CGRA 8-bit 2 GHz
cadence_xtensa Cadence Xtensa-NX 16-bit 1.5 GHz

3D-Stacked (3 profiles)

Physics: 3D integration via through-silicon vias (TSVs), hybrid bonding, or micro-bumps. Dramatically reduces interconnect length and enables heterogeneous integration (logic + memory on same stack).

Profile Vendor Family Width Freq
tsmc_soic TSMC SoIC-3D 16-bit 2 GHz
intel_foveros Intel Foveros-3D 16-bit 3 GHz
amd_3d_vcache AMD 3D-V-Cache 16-bit 4.5 GHz

Edge MCU / TinyML (5 profiles)

Physics: Standard ARM Cortex-M and RISC-V microcontrollers with hardware neural accelerators. Target: <1mW inference at the sensor edge.

Profile Vendor Family Width Freq
rp2040 Raspberry Pi RP2040 16-bit 133 MHz
esp32_s3 Espressif ESP32-S3 16-bit 240 MHz
stm32_h7 STMicro STM32H7 16-bit 480 MHz
nrf5340 Nordic nRF5340 16-bit 128 MHz
max78000 Maxim/ADI MAX78000 8-bit 100 MHz
Python
from sc_neurocore.compiler.platforms import get_profile

p = get_profile("max78000")
print(f"Built-in CNN accelerator: {p.notes}")

Biological, Electrochemical, and Wafer-Scale Platforms

Biological / Wetware (2 profiles)

Physics: Living organoid co-processors. FinalSpark uses human iPSC neuron cultures as computational substrates. Cortical Labs (DishBrain) demonstrated game-playing biological neural networks.

Profile Vendor Family Width
finalspark_neuroplatform FinalSpark Neuroplatform 16-bit
cortical_labs_dishbrain Cortical Labs DishBrain 16-bit

Compilation notes: - Output is stimulation protocol, not RTL - Time constants are biological (ms–s scale) - Stochastic behaviour — no deterministic guarantee

Electrochemical / Memristive (3 profiles)

Physics: Electrochemical RAM (ECRAM) and Phase-Change RAM (PCRAM) enable analog synaptic weight storage with tuneable conductance states. Ideal for in-situ training.

Profile Vendor Family Width
ibm_ecram IBM ECRAM-AnalogAI 8-bit
samsung_pcram Samsung PCRAM 8-bit
stanford_ecram Stanford ECRAM-Research 8-bit

Wafer-Scale (3 profiles)

Physics: Entire silicon wafers used as single chips. Cerebras WSE-3 has 4 trillion transistors, 900,000 cores. Tesla Dojo uses custom die-to-die interconnect for distributed neural training.

Profile Vendor Family Width Freq
cerebras_wse3_ws Cerebras WSE-3-WS 16-bit 1 GHz
tesla_dojo3 Tesla Dojo-3 16-bit 2 GHz
tachyum_prodigy Tachyum Prodigy-2nm 16-bit 5.5 GHz

Analog Mixed-Signal (2 profiles)

Physics: Compute-at-sensor architectures. Aspinity AML100 performs analog feature extraction before ADC, eliminating >90% of data movement. Renesas AnalogAI integrates analog MAC arrays.

Profile Vendor Family Width
aspinity_aml100 Aspinity AML100 8-bit
renesas_analog_ai Renesas AnalogAI 8-bit

Memory-Centric and Quantum-Inspired Platforms

RRAM / Memristive Crossbar (3 profiles)

Physics: Resistive RAM crossbar arrays perform in-situ matrix-vector multiplication using Ohm's law (V=IR) and Kirchhoff's current law. Weebit Nano demonstrates 200 TOPS/W, licensed to TI and Onsemi.

Profile Vendor Family Width
weebit_reram Weebit Nano ReRAM-ACiM 8-bit
crossbar_rram Crossbar ReRAM-1T1R 8-bit
adesto_cbram Adesto CBRAM 8-bit

Compilation notes: - Weights stored as conductance states (analog) - Use drift compensation (§32) for long-term reliability - 8-bit is typical due to conductance resolution limits

SRAM Compute-in-Memory (2 profiles)

Physics: Digital CIM using standard 6T SRAM cells modified for in-memory Boolean/MAC operations. TSMC and Samsung offer foundry- qualified CIM macro IP at N7 and SF3 nodes.

Profile Vendor Family Width Freq
tsmc_cim_n7 TSMC CIM-N7 8-bit 1 GHz
samsung_cim_sf3 Samsung CIM-SF3 8-bit 900 MHz

Cryogenic CMOS (2 profiles)

Physics: Standard CMOS operated at 4K for quantum chip control. Device physics change significantly at cryogenic temperatures (threshold shift, carrier freeze-out, reduced leakage).

Profile Vendor Family Width Freq
intel_horse_ridge Intel Horse-Ridge-II 16-bit 6 GHz
google_cryo_ctrl Google Cryo-Controller 16-bit 4 GHz

DNA / Molecular (2 profiles)

Physics: DNA base-pair gated logic and perovskite-DNA hybrid synaptic devices. Microsoft demonstrated enzymatic DNA synthesis for archival compute. ASU achieved CMOS-DNA integration.

Profile Vendor Family Width
microsoft_dna_store Microsoft DNA-Storage 8-bit
asu_dna_perovskite ASU DNA-Perovskite 8-bit

Quantum Neuromorphic (2 profiles)

Physics: Quantum reservoir computing and quantum SNNs using superconducting transmon qubits (IBM) or trapped-ion all-to-all connectivity (IonQ).

Profile Vendor Family Width
ibm_qnn IBM Quantum-NN 16-bit
ionq_trapped_ion IonQ Trapped-Ion-QNN 16-bit

Interconnect, Acoustic, Fluidic, and Space Platforms

Optical Interconnect / CPO (2 profiles)

Physics: Silicon photonic I/O chiplets replacing electrical interconnect with optical. Ayar Labs TeraPHY delivers 8 Tbps bidirectional, UCIe-compatible. Enables rack-scale SNN.

Profile Vendor Family Width Freq
ayar_teraphy Ayar Labs TeraPHY 16-bit 25 GHz
intel_cpo Intel CPO 16-bit 20 GHz

Acoustic / Phononic (2 profiles)

Physics: Acoustic wave reservoir computing using MEMS resonator arrays. Mechanical nonlinearity provides natural activation functions. Zero digital power consumption during inference.

Profile Vendor Family Width
mit_phononic MIT Phononic-NN 8-bit
caltech_mems_nn Caltech MEMS-NN 8-bit

Fluidic / Microfluidic (2 profiles)

Physics: Droplet-based and pressure-driven bistable logic gates for chemical/biological neural computation. Lab-on-chip applications for in-vivo diagnostic neural inference.

Profile Vendor Family Width
stanford_microfluidic Stanford µFluidic-NN 8-bit
eth_fluidic_logic ETH Zurich Fluidic-Logic 8-bit

Space-Qualified (4 profiles)

Physics: Radiation-hardened processors qualified for TID (Total Ionizing Dose) and SEE (Single Event Effects) tolerance. Deployed on ISS, Mars rovers, and deep-space missions.

Profile Vendor Family Width Freq
bae_rad750_sq BAE Systems RAD750 32-bit 200 MHz
seakr_sbc SEAKR SBC-SpaceAI 16-bit 400 MHz
vorago_va10820 Vorago VA10820 16-bit 100 MHz
frontgrade_leon5 Frontgrade LEON5-FT 32-bit 250 MHz

Compilation notes: - Use TMR (Triple Modular Redundancy) via §7 SEU hardening - Combine with §50 fault tree for DO-254 Level A certification - Use supply chain risk scorer (§36) for ITAR compliance

Python
from sc_neurocore.compiler.platforms import get_profile
from sc_neurocore.compiler.intelligence import (
    score_supply_chain_risk, generate_fault_tree,
)

p = get_profile("bae_rad750_sq")
risk = score_supply_chain_risk("bae_rad750_sq")
ft = generate_fault_tree("sc_lif", {"v": "-(v)/tau + I"})
print(f"Risk: {risk.overall_risk}")
print(f"Fault tree events: {len(ft.basic_events)}")

Sovereign, Organic, and Magnonic Platforms

Magnonic / Skyrmion (3 profiles)

Physics: Magnetic skyrmions are topologically protected spin textures that behave as quasi-particles. Their nonlinear dynamics, ultra-low switching energy (~10⁻²⁰ J), and emergent collective behaviour make them ideal reservoir computing substrates. Spin-wave (magnon) interference provides natural nonlinear activation without transistors.

Profile Vendor Family Width
tum_skyrmion TU Munich SkyANN-v1 8-bit
kaist_spinwave KAIST SpinWave-RC 8-bit
imec_mtj_reservoir imec MTJ-Reservoir 8-bit

Compilation notes: - Reservoir computing mode: only readout layer trained - Use 8-bit widths (sub-fJ switching limits precision) - Ideal for temporal signal processing (EEG, vibration)

Python
from sc_neurocore.compiler.platforms import get_profile

p = get_profile("tum_skyrmion")
print(f"Class: {p.platform_class}")  # magnonic

Organic Bioelectronic (2 profiles)

Physics: Organic Electrochemical Transistors (OECTs) use mixed ionic/electronic conduction in PEDOT:PSS to create artificial synapses that operate in aqueous environments. Enables direct neural tissue interfacing for in-vivo bioelectronic medicine.

Profile Vendor Family Width
cambridge_oect Cambridge OECT-Synapse 8-bit
linkoping_organic Linköping Organic-NN 8-bit

Compilation notes: - Output is stimulation/recording protocol, not RTL - Wet computing: ionic time constants (100 ms – 10 s) - Biocompatible — no heavy metals - Use HIL calibration (§62) for analog drift compensation

RISC-V Sovereign AI (5 profiles)

Physics: Open-ISA RISC-V processors with vector AI extensions. No ITAR/EAR restrictions. Enables data-sovereign and export-control-free deployment for government, defence, and critical infrastructure.

Profile Vendor Family Width Freq
sifive_x280_ai SiFive X280-AI 16-bit 2 GHz
esperanto_et_soc Esperanto ET-SoC-1 8-bit 1 GHz
ventana_veyron_ai Ventana Veyron-V2 16-bit 3.6 GHz
tenstorrent_ascalon Tenstorrent Ascalon 16-bit 4 GHz
andes_ax45mpv Andes AX45MPV 16-bit 1.5 GHz

Compilation notes: - Open ISA: no license fees or export restrictions - Use UCIe protocol mapper (§64) for chiplet-based designs - Combine with SBOM generator (§61) for EU CRA compliance - Supply chain risk score (§36) will be LOW for all RISC-V

Python
from sc_neurocore.compiler.platforms import get_profile
from sc_neurocore.compiler.intelligence import score_supply_chain_risk

p = get_profile("sifive_x280_ai")
risk = score_supply_chain_risk("sifive_x280_ai")
print(f"Open ISA — Risk: {risk.overall_risk}")

Custom Profile Registration

Method 1: TOML File (Zero Code Changes)

Create a .toml file with your custom profiles:

TOML
# my_lab_profiles.toml
[[profile]]
name = "my_custom_asic"
vendor = "UniversityLab"
family = "ASIC-v1"
platform_class = "asic"
data_width = 24
fraction = 12
overflow = "saturate"
rounding = "nearest"
max_freq_mhz = 500
notes = "Custom 24-bit ASIC for cortical simulation."

[[profile]]
name = "my_fpga_board"
vendor = "UniversityLab"
family = "Custom-FPGA"
platform_class = "fpga"
data_width = 16
fraction = 8
overflow = "wrap"
rounding = "truncate"
dsp_block = "DSP48E2"
dsp_mult_a = 18
dsp_mult_b = 27
max_freq_mhz = 250
notes = "Custom FPGA board with Xilinx UltraScale+."
Python
from sc_neurocore.compiler.intelligence import load_profiles_from_toml

loaded = load_profiles_from_toml("my_lab_profiles.toml")
print(f"Loaded: {loaded}")  # ['my_custom_asic', 'my_fpga_board']

Method 2: Runtime Discovery Hook (Vendor SDK)

Python
from sc_neurocore.compiler.intelligence import (
    register_platform_hook, discover_platforms,
)
from sc_neurocore.compiler.platforms import HardwareProfile

def vendor_discovery():
    """Auto-detect connected hardware and return profiles."""
    return [HardwareProfile(
        name="detected_board", vendor="AutoDetect",
        family="Board-v1", platform_class="fpga",
        data_width=16, fraction=8,
        overflow="saturate", rounding="nearest",
    )]

register_platform_hook(vendor_discovery)
discovered = discover_platforms()

Method 3: Auto-Construct from Spec Sheet

Python
from sc_neurocore.compiler.platforms import HardwareProfile

# Any future chip — one function call:
p = HardwareProfile.from_constraints(
    "my_2030_chip",
    vendor="FutureVendor",
    platform_class="custom",
    max_power_budget_mw=5,
    min_precision_bits=8,
)
# Automatically registered and ready to compile against

Frontier Paradigm Platforms

Wetware / Biological (2 profiles)

Physics: Living organoid co-processors interfaced via high-density Multi-Electrode Arrays (MEAs). FinalSpark uses living spherical brain organoids for closed-loop biocomputing. Cortical Labs (DishBrain) demonstrates embodied intelligence via active biological neural cultures.

Profile Vendor Family Width
finalspark_neuroplatform FinalSpark Neuroplatform 8-bit
cortical_labs_dishbrain Cortical Labs DishBrain 8-bit

Compilation notes: - Uses the map_wetware_mea (§79) intelligence feature to translate SNN topology into spatio-temporal stimulations.

Molecular / Chemical (2 profiles)

Physics: Computation and storage within synthetic DNA base pairs and enzymatic reactions. Biomemory maps ultra-high-density data (such as billion-parameter network weights) into physical DNA storage at near-zero static energy. Catalog utilizes parallel search within DNA liquid solutions.

Profile Vendor Family Width
biomemory_dna Biomemory DNA-Storage 8-bit
catalog_dna_compute Catalog Shannon 8-bit

Reversible / Adiabatic (2 profiles)

Physics: Operating at the Landauer limit of energy dissipation. Logic gates (like Toffoli and Fredkin) preserve information perfectly, allowing energy to be recovered rather than dissipated as heat. Requires multi-phase trapezoidal resonant clocking (§82).

Profile Vendor Family Width
superconducting_aqfp Yokohama Univ AQFP 16-bit
scrl_logic Generic SCRL 16-bit

Microfluidic / Mechanical (2 profiles)

Physics: Fluid dynamics and nonlinear mechanical oscillators acting as physical computational substrates. Nanofluidic 2D ionic channels physically emulate biological ion exchange using water/ion flow.

Profile Vendor Family Width
nanofluidic_logic EPFL Ion-Channel 8-bit
mems_neuromorphic Generic MEMS-Resonator 8-bit

Summary of the 39 platform classes supported by SC-NeuroCore:

ID Class Name Profiles Description Target
1 fpga 27 Traditional FPGAs (Xilinx/Intel) Production
2 asic 13 Custom silicon Production
3 neuromorphic 11 SNN chips (Loihi, TrueNorth) Production
4 pim 8 Processing-in-memory Production
5 quantum 6 Superconducting/Ion/Optical Research
6 optical 6 Silicon photonics Pre-production
7 analog 6 Continuous-time analog Research
8 memristive 6 Crossbar arrays Research
9 emerging 3 Hybrid/novel Research
10 superconducting 3 SFQ/AQFP at 4K Research
11 spintronic 2 STT/SOT-MRAM Pre-production
12 ferroelectric 2 FeFET/FeRAM Pre-production
13 cgra 3 Reconfigurable array Production
27 fluidic 2 Microfluidic logic Research
28 space_qualified 4 Rad-hard processors Deployed
29 magnonic 3 Skyrmion/spin-wave Research
30 organic_bioelectronic 2 OECT wet computing Research
31 risc_v_sovereign 5 Open ISA AI cores Production
32 thermodynamic 2 EBM thermal equilibration Research
33 probabilistic 2 p-Bit sMTJ Research
34 polariton 2 Bose-Einstein condensate Research
35 metamaterial 2 Passive wave propagation Research
36 wetware 2 Living organoids (MEA) Research
37 molecular 2 DNA / Enzymatic compute Research
38 reversible 2 Adiabatic / Landauer limit Research
39 microfluidic 2 Nanofluidic / MEMS Research
Total 191

Further Reading