SoC Recovery Control Mechanisms

• SoC Recovery Control is defined as integrated hardware/software solutions that autonomously detect, mitigate, and restore system functions after faults, attacks, or disturbances.
• Architectural approaches like CARE and SHP implement fine-grained recovery through secure flash authentication and majority-voting mechanisms, achieving minimal performance overhead.
• In energy systems, autonomous SOC recovery extends to balancing state-of-charge in microgrids and hybrid storage, enhancing cycle life and efficiency.

System-on-Chip (SoC) Recovery Control refers to integrated mechanisms and control architectures that enable autonomous detection, mitigation, and restoration of SoC functional and security states following faults, attacks, or transient disturbances. These recovery solutions are characterized by on-chip, hardware/software co-designed logic that operates with minimal or no external intervention and typically target critical reliability, safety, or security requirements in domains such as IoT, automotive, renewable power management, and mission-critical embedded computing.

1. Architectural Approaches to SoC Recovery Control

SoC recovery control frameworks span diverse domains, but converge on certain architectural principles. Notable implementations include:

• CARE (Code Authentication and Resilience Engine) in RISC-V (Dave et al., 2021): A hybrid hardware–software block that augments secure boot: software frames from external flash are authenticated (HMAC-SHA256) and, on detection of any corruption, the Resilience Engine restores only the corrupted region from a ROM-resident golden image. CARE integrates with the Ibex core, leveraging RISC-V Physical Memory Protection (PMP) features for isolation and using secureIbex extensions (e.g., randomized NOP, ECC reads) for resilience against side-channel and fault injection.
• System Hyper-Pipelining (SHP) for In-field Self-repair (Strauch, 2024): Combines barrel context switching and C-slow retiming to implement multi-threaded, pipeline-duplicated logic; integrates SEU detection and ultra-fast recovery via majority-voting across redundant context memories, orchestrated by a central Thread Controller (TC) which interleaves application and test threads while performing rapid repair.

These architectures emphasize:

• Fine-grained recovery (recovering only impacted frames/contexts).
• Hardware-enforced isolation and detection (PMP, comparators, ECC).
• Minimal resource and performance overhead (<8% in CARE; 1.1×–2.3× performance-per-area gains in SHP).

2. SOC Recovery in Security-Critical Boot and Runtime (CARE)

The CARE design implements the following protocol:

• On reset, the FSBL (First-Stage Boot Loader) is fetched from secure ROM, initializing bus, flash, and PMP registers.
• The flash image is streamed in discrete, 1 KB frames. For each frame, the Code Authentication unit generates a SHA256 digest and HMAC, comparing against golden and header values.
• If authentication fails, the Resilience Engine locates and re-flashes the corrupted frame from secure ROM, reapplying PMP locks.
• State transition formalism: S = {INIT, VERIFY, RECOVER, DONE}, with transitions governed by CA outcomes.
• Verification chain formalized in:

$$V_0 = \mathrm{True},\quad V_{i} = V_{i-1} \wedge I_i(f_i) \wedge S_i(f_i),\quad i=1\ldots n,$$

where $I_i, S_i$ are integrity and authenticity checks per frame.

The overheads are strictly bounded: 334 μs per 1 KB frame recovery, 8% boot-time/energy, and 5 KB ROM for recovery images. The separation of flash regions (PMP: $R=1, W=0, X=0$) prevents unauthorized modifications during recovery. Hardware acceleration (crypto core) delivers 16× speedup and 92% energy reduction over software hashing.

3. Autonomous State-of-Charge (SOC) Recovery in Energy Systems

In hybrid hydrogen electrolyzer–supercapacitor systems ("HHESS"), recovery control addresses the SOC of supercapacitors (SC) under grid disturbances (Lin et al., 3 Jan 2026):

• The SC branch is assigned autonomous SOC recovery as a stringent control objective: after a transient event, the SC must return exactly to its pre-event charge, with no scheduler or communication overhead.
• This is achieved by a capacitive integral droop (CID) controller, $$G_{\mathrm{CID}}(s) = \frac{1}{s \zeta} \frac{s+k}{s}$$, yielding a SC power transfer function with zero DC gain:

$$\Delta Q_{\mathrm{SC}} = \int_0^\infty P_s(t) dt = 0,\quad \mathrm{so~that~} \mathrm{SOC}_{\mathrm{SC}}(\infty) = \mathrm{SOC}_{\mathrm{SC}}(0)$$

• Controller tuning enforces bandwidth and damping via closed-form algebraic equations to determine $\gamma$ and $k$.

This approach preserves SC cycle life far beyond conventional droop controls (10× more cycles to end-of-life; zero transient drift). Offloading high-frequency transients to SC reduces losses in electrolyzers (3–5% Faraday improvement), extends component lifetimes, and increases available inertia support.

4. Distributed SOC Recovery in Microgrid Energy Storage

Distributed frequency restoration and SOC balancing control for battery energy storage in microgrids is formulated as a multi-agent, consensus-driven protocol (Yu et al., 2021):

• Each storage agent maintains states $s_i$ (SOC), $q_i$ (power allocation), and $\lambda_i$ (reference ratio).
• SOC balancing and frequency restoration are unified via second-order consensus:

$\dot{s}_i = q_i;\quad \dot{q}_i = u_i$

with $u_i$ derived from a finite-time control law using coupling signals $\phi_i = \alpha \xi_i + \beta \zeta_i$, where $\xi, \zeta$ are graph-weighted consensus errors.

• Lyapunov and homogeneous approximation theory provide accelerated convergence and fixed-time guarantees, independent of initial condition, leveraging composite $V$ functions:

$\dot{V} \leq - (V^p + V^q)^r$

• Event-triggered communication is embedded to minimize network load and avoid Zeno behavior:

$\| e_i(t) \| \leq \rho \| \phi_i(t) \|$

Only when local errors exceed $\rho$-scaled thresholds does an agent broadcast updates.

Simulation validates rapid settling (SoC synchronization in 5s, frequency in 0.2s) and communication reduction. Local constraints (SoC bounds, integrator saturation, frequency deadband) are handled natively in agent logic.

5. On-line and In-field Fault Recovery in Mission-Critical SoCs

In-field, non-interfering recovery is critical in domains affected by aging defects (delay variation, stuck-at faults) and transient events (SEU) (Strauch, 2024):

• System Hyper-Pipelining (SHP) with SEU Detection & Recovery Units (SDRU) implements execution redundancy and comparator-based mismatch detection. Recovery is achieved by majority voting and overwriting only erroneous contexts, followed by immediate re-execution.
• RTL ATPG self-test programs are scheduled as threads, achieving 100% stuck-at coverage with minimal runtime interference (Test-Cycles-per-Net ~0.6–1.5).
• Performance-per-area gains of 1.1×–2.3× (ASIC/FPGA). Recovery latency is bounded by a single fast pipeline cycle ($$T_\text{recovery} \leq T_\text{micro} + T_\text{comp} + T_\text{mem\_wr}$$).

This framework eliminates the need for OS-level rollback, maintains mission data integrity, and is compliant with automotive (ISO26262), space, and defense reliability requirements.

6. Isolation, Stability, and Fine-Grained Recovery Requirements

Across application domains, isolation of critical memory and logic regions is enforced via hardware primitives (e.g., RISC-V PMP in CARE, dual-port context RAM in SHP). Stability of recovery loops in energy systems is established via large-signal Brayton–Moser-based mixed-potential theory (Lin et al., 3 Jan 2026):

• The composite system is stable if eigenvalue criteria $\mu_1 + \mu_2 > 0$ (interpolating converter inductance and capacitor matrices).
• The tuning of droop coefficients $(\alpha, \beta, \gamma, \zeta, k)$ ensures passivity and prevents destabilization, with stability boundaries directly mapped to grid/network parameters.

Fine-grained recovery architectures avoid full system rollback—restoring only affected regions (CA/RE frame-level in CARE, per-thread context in SHP), minimizing performance and reliability impact.

Architecture	Recovery Target	Detection Mechanism	Recovery Action
CARE (Dave et al., 2021)	Flash image	HMAC-SHA256 CA + chain	Frame re-flash from ROM
SHP (Strauch, 2024)	Register context	SEU comparator, SBST	Majority overwrite, thread re-exec
HESS (Lin et al., 3 Jan 2026)	SC SOC	Autonomous CID droop	Zero net SC charge over event
Microgrid (Yu et al., 2021)	Battery SOC	Distributed consensus	Finite-time controller

7. Limitations, Prospective Directions, and Critical Design Insights

System-level recovery control solutions bring high reliability with low overhead, but require:

• Hardware redesign for comparators, context access, and recovery engines.
• Precise controller tuning for stability and minimal drift (energy systems).
• Advanced self-test thread libraries for comprehensive coverage without disrupting real-time deadlines.

Future research directions identified include:

• Coordinated multi-core recovery (CARE).
• Dynamic golden image update and over-the-air attestation (CARE).
• Hardware acceleration of resilience routines (CARE).
• Extension to broader energy systems, leveraging autonomous SOC recovery for massive cycle life and loss reduction (HESS).
• Expansion of event-triggered, consensus-based recovery for dense, resource-constrained microgrid deployments.

These technologies collectively advance the "resilience" dimension in SoC design, providing continuous, fine-grained, autonomous restoration of system safety, integrity, and operational availability with quantifiable and bounded cost (Dave et al., 2021, Lin et al., 3 Jan 2026, Yu et al., 2021, Strauch, 2024).